{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Using Fastai  Vision+Tabular On Petfinder.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "GdIYjywtP9xl",
        "colab_type": "code",
        "outputId": "0b9f77b9-e91a-4554-e550-68ee2d08eba8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "!curl -s https://course.fast.ai/setup/colab | bash\n",
        "from fastai.vision import *"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updating fastai...\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7lGoifTdQTtu",
        "colab_type": "code",
        "outputId": "f8cb8632-11c1-46c4-a812-08b459494710",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HITLPt03Qr2G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BASE = \"/content/drive/My Drive/fastai-v3/data/petadoption\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "YECzTlUNP8l0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "import os\n",
        "#print(os.listdir(BASE + \"/train_images\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FdCPk1MQRHOK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "#!KAGGLE_USERNAME=gdoteof KAGGLE_KEY=##### kaggle competitions download -c petfinder-adoption-prediction\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qX0xzgb6SBHz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "42ee384e-983c-47af-fc4c-c3eadcb3ce76"
      },
      "cell_type": "code",
      "source": [
        "!ls -l /content\n",
        "!mkdir -p \"{BASE}\"\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 8\n",
            "lrwxrwxrwx 1 root root   18 Mar 20 21:03 data -> /root/.fastai/data\n",
            "drwxr-xr-x 3 root root 4096 Mar 20 21:37 dir_001\n",
            "drwx------ 3 root root 4096 Mar 20 22:51 drive\n",
            "lrwxrwxrwx 1 root root   19 Mar 20 21:03 models -> /root/.torch/models\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R-eL_EvUXQls",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BIcA5lLcSN9d",
        "colab_type": "code",
        "outputId": "95ddd26e-ea1e-4a66-f7df-6865791ffe4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "cell_type": "code",
      "source": [
        "!ls -l \"{BASE}\""
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 2045772\n",
            "drwx------ 2 root root       4096 Mar 20 21:59 models\n",
            "drwx------ 2 root root       4096 Mar 20 21:22 test\n",
            "drwx------ 2 root root       4096 Mar 20 18:56 test_images\n",
            "-rw------- 1 root root  410128757 Mar 20 18:35 test_images.zip\n",
            "drwx------ 2 root root       4096 Mar 20 19:03 test_metadata\n",
            "-rw------- 1 root root   14602132 Mar 20 18:35 test_metadata.zip\n",
            "drwx------ 2 root root       4096 Mar 20 19:07 test_sentiment\n",
            "-rw------- 1 root root    3254800 Mar 20 18:35 test_sentiment.zip\n",
            "-rw------- 1 root root     643897 Mar 20 18:35 test.zip\n",
            "drwx------ 2 root root       4096 Mar 20 20:24 train\n",
            "drwx------ 2 root root       4096 Mar 20 20:23 train_images\n",
            "-rw------- 1 root root 1595336815 Mar 20 18:35 train_images.zip\n",
            "drwx------ 2 root root       4096 Mar 20 19:10 train_metadata\n",
            "-rw------- 1 root root   56196604 Mar 20 18:35 train_metadata.zip\n",
            "drwx------ 2 root root       4096 Mar 20 20:14 train_sentiment\n",
            "-rw------- 1 root root   11878318 Mar 20 18:35 train_sentiment.zip\n",
            "-rw------- 1 root root    2790732 Mar 20 18:35 train.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AY7myOehSpj7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#!mv train* \"{BASE}\"\n",
        "#!mv test*  \"{BASE}\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "c44fbb18cdd7925d542ee340645b8edb4f46f5df",
        "id": "7b439bZqP8l9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**My Code**"
      ]
    },
    {
      "metadata": {
        "id": "zgOipmCtY7l3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "f = \"train_sentiment\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MlxYpSrFTAbR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p \"{BASE}/{f}\"\n",
        "#!unzip -q \"{BASE}/{f}.zip\" -d \"{BASE}/{f}\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qVlhXg2dwllb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "2d9a6e14-0cc4-47c7-a2cd-ab89f33378dc"
      },
      "cell_type": "code",
      "source": [
        "!echo \"{BASE}/test\"\n",
        "!ls -l \"{BASE}/test\""
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/fastai-v3/data/petadoption/test\n",
            "total 1884\n",
            "-rw------- 1 root root   47396 Dec 26 19:17 sample_submission.csv\n",
            "-rw------- 1 root root 1881101 Dec 26 19:17 test.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f7df5c4785d85fded12b261f071cecc3af04567e",
        "id": "9lQp0Av5P8l_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "from fastai.tabular import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b651933a46cbeab940529fd4d69433029bd8715a",
        "id": "SVNYOHqqP8mE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "path = Path(BASE)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ddab29b45550042d4ecb3381cf8494888389d9d7",
        "id": "qix6DVlqP8mJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "path_train_img = path/'train_images';\n",
        "path_test_img = path/'test_images';\n",
        "path_train = path/'train';\n",
        "path_test = path/'test'; \n",
        "path_train_sentiment = path/'train_sentiment';\n",
        "path_test_sentiment = path/'test_sentiment';\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SaB9QrCMP8mQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Can we predict adoptions just from pictures?"
      ]
    },
    {
      "metadata": {
        "id": "CLx854VM0Y10",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_imgs = path_train_img.ls()\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "O89qR6gbP8mR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_img = pd.DataFrame(columns=['Image'], data=path_train_img.ls())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GWk5PXON4Vn0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#df_img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-Ueae0V71eSr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ee7cf7c2-1b11-4ce3-e535-35cb43a8c7bc"
      },
      "cell_type": "code",
      "source": [
        "df_img['Image'] = df_img['Image'].astype(np.str).replace(to_replace='/content/drive/My Drive/fastai-v3/data/petadoption/',value='./', regex=True)\n",
        "df_img.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>./train_images/ce0294cec-3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>./train_images/2f4c6ada3-1.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>./train_images/d4ec04b8e-19.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>./train_images/326c9c979-5.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>./train_images/c670993fc-14.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Image\n",
              "0   ./train_images/ce0294cec-3.jpg\n",
              "1   ./train_images/2f4c6ada3-1.jpg\n",
              "2  ./train_images/d4ec04b8e-19.jpg\n",
              "3   ./train_images/326c9c979-5.jpg\n",
              "4  ./train_images/c670993fc-14.jpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "z5bJ82tuP8mW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_tab = pd.read_csv(path_train/'train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "-z-d9bvBP8mZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "outputId": "51c2aad6-f735-431c-dd5b-52d5da4a5c6b"
      },
      "cell_type": "code",
      "source": [
        "df_tab.head().transpose()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>Nibble</td>\n",
              "      <td>No Name Yet</td>\n",
              "      <td>Brisco</td>\n",
              "      <td>Miko</td>\n",
              "      <td>Hunter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <th>Breed1</th>\n",
              "      <td>299</td>\n",
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              "      <td>307</td>\n",
              "      <td>307</td>\n",
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              "    <tr>\n",
              "      <th>Breed2</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>7</td>\n",
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              "      <td>7</td>\n",
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              "      <th>Color3</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>MaturitySize</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FurLength</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vaccinated</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dewormed</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sterilized</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fee</th>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>41326</td>\n",
              "      <td>41401</td>\n",
              "      <td>41326</td>\n",
              "      <td>41401</td>\n",
              "      <td>41326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RescuerID</th>\n",
              "      <td>8480853f516546f6cf33aa88cd76c379</td>\n",
              "      <td>3082c7125d8fb66f7dd4bff4192c8b14</td>\n",
              "      <td>fa90fa5b1ee11c86938398b60abc32cb</td>\n",
              "      <td>9238e4f44c71a75282e62f7136c6b240</td>\n",
              "      <td>95481e953f8aed9ec3d16fc4509537e8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VideoAmt</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>Nibble is a 3+ month old ball of cuteness. He ...</td>\n",
              "      <td>I just found it alone yesterday near my apartm...</td>\n",
              "      <td>Their pregnant mother was dumped by her irresp...</td>\n",
              "      <td>Good guard dog, very alert, active, obedience ...</td>\n",
              "      <td>This handsome yet cute boy is up for adoption....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetID</th>\n",
              "      <td>86e1089a3</td>\n",
              "      <td>6296e909a</td>\n",
              "      <td>3422e4906</td>\n",
              "      <td>5842f1ff5</td>\n",
              "      <td>850a43f90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhotoAmt</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdoptionSpeed</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                               0  \\\n",
              "Type                                                           2   \n",
              "Name                                                      Nibble   \n",
              "Age                                                            3   \n",
              "Breed1                                                       299   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         1   \n",
              "Color1                                                         1   \n",
              "Color2                                                         7   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   1   \n",
              "FurLength                                                      1   \n",
              "Vaccinated                                                     2   \n",
              "Dewormed                                                       2   \n",
              "Sterilized                                                     2   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                          100   \n",
              "State                                                      41326   \n",
              "RescuerID                       8480853f516546f6cf33aa88cd76c379   \n",
              "VideoAmt                                                       0   \n",
              "Description    Nibble is a 3+ month old ball of cuteness. He ...   \n",
              "PetID                                                  86e1089a3   \n",
              "PhotoAmt                                                       1   \n",
              "AdoptionSpeed                                                  2   \n",
              "\n",
              "                                                               1  \\\n",
              "Type                                                           2   \n",
              "Name                                                 No Name Yet   \n",
              "Age                                                            1   \n",
              "Breed1                                                       265   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         1   \n",
              "Color1                                                         1   \n",
              "Color2                                                         2   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   2   \n",
              "FurLength                                                      2   \n",
              "Vaccinated                                                     3   \n",
              "Dewormed                                                       3   \n",
              "Sterilized                                                     3   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                            0   \n",
              "State                                                      41401   \n",
              "RescuerID                       3082c7125d8fb66f7dd4bff4192c8b14   \n",
              "VideoAmt                                                       0   \n",
              "Description    I just found it alone yesterday near my apartm...   \n",
              "PetID                                                  6296e909a   \n",
              "PhotoAmt                                                       2   \n",
              "AdoptionSpeed                                                  0   \n",
              "\n",
              "                                                               2  \\\n",
              "Type                                                           1   \n",
              "Name                                                      Brisco   \n",
              "Age                                                            1   \n",
              "Breed1                                                       307   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         1   \n",
              "Color1                                                         2   \n",
              "Color2                                                         7   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   2   \n",
              "FurLength                                                      2   \n",
              "Vaccinated                                                     1   \n",
              "Dewormed                                                       1   \n",
              "Sterilized                                                     2   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                            0   \n",
              "State                                                      41326   \n",
              "RescuerID                       fa90fa5b1ee11c86938398b60abc32cb   \n",
              "VideoAmt                                                       0   \n",
              "Description    Their pregnant mother was dumped by her irresp...   \n",
              "PetID                                                  3422e4906   \n",
              "PhotoAmt                                                       7   \n",
              "AdoptionSpeed                                                  3   \n",
              "\n",
              "                                                               3  \\\n",
              "Type                                                           1   \n",
              "Name                                                        Miko   \n",
              "Age                                                            4   \n",
              "Breed1                                                       307   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         2   \n",
              "Color1                                                         1   \n",
              "Color2                                                         2   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   2   \n",
              "FurLength                                                      1   \n",
              "Vaccinated                                                     1   \n",
              "Dewormed                                                       1   \n",
              "Sterilized                                                     2   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                          150   \n",
              "State                                                      41401   \n",
              "RescuerID                       9238e4f44c71a75282e62f7136c6b240   \n",
              "VideoAmt                                                       0   \n",
              "Description    Good guard dog, very alert, active, obedience ...   \n",
              "PetID                                                  5842f1ff5   \n",
              "PhotoAmt                                                       8   \n",
              "AdoptionSpeed                                                  2   \n",
              "\n",
              "                                                               4  \n",
              "Type                                                           1  \n",
              "Name                                                      Hunter  \n",
              "Age                                                            1  \n",
              "Breed1                                                       307  \n",
              "Breed2                                                         0  \n",
              "Gender                                                         1  \n",
              "Color1                                                         1  \n",
              "Color2                                                         0  \n",
              "Color3                                                         0  \n",
              "MaturitySize                                                   2  \n",
              "FurLength                                                      1  \n",
              "Vaccinated                                                     2  \n",
              "Dewormed                                                       2  \n",
              "Sterilized                                                     2  \n",
              "Health                                                         1  \n",
              "Quantity                                                       1  \n",
              "Fee                                                            0  \n",
              "State                                                      41326  \n",
              "RescuerID                       95481e953f8aed9ec3d16fc4509537e8  \n",
              "VideoAmt                                                       0  \n",
              "Description    This handsome yet cute boy is up for adoption....  \n",
              "PetID                                                  850a43f90  \n",
              "PhotoAmt                                                       3  \n",
              "AdoptionSpeed                                                  2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "YWaabW9YP8mf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "PetID is the lookup(?) for the images."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "TmAxL8voP8mh",
        "colab_type": "code",
        "outputId": "f3df4fee-8da8-4aa5-af4f-3076d3b9b05f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "def getIdFromImgPath(x):  return re.search('.*\\/(\\w*)', str(x), re.IGNORECASE).group(1)\n",
        "\"sloppily converts path to string, then pulls the id out\"\n",
        "path2 = df_img.iloc[0].Image\n",
        "print(path2)\n",
        "print(getIdFromImgPath(df_img.iloc[0].Image))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./train_images/ce0294cec-3.jpg\n",
            "ce0294cec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ju1Q7XpOP8mp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "So, now we need to use the img id to associate each image with an adoption speed"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "IWNkswkuP8mq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_img['petId'] = df_img['Image'].map(getIdFromImgPath)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "5ThtgGEIP8mt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "1a87fbc2-ece4-48d2-aab3-5ebb12cef3b5"
      },
      "cell_type": "code",
      "source": [
        "df_img.head().transpose()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Image</th>\n",
              "      <td>./train_images/ce0294cec-3.jpg</td>\n",
              "      <td>./train_images/2f4c6ada3-1.jpg</td>\n",
              "      <td>./train_images/d4ec04b8e-19.jpg</td>\n",
              "      <td>./train_images/326c9c979-5.jpg</td>\n",
              "      <td>./train_images/c670993fc-14.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petId</th>\n",
              "      <td>ce0294cec</td>\n",
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            "text/plain": [
              "                                    0                               1  \\\n",
              "Image  ./train_images/ce0294cec-3.jpg  ./train_images/2f4c6ada3-1.jpg   \n",
              "petId                       ce0294cec                       2f4c6ada3   \n",
              "\n",
              "                                     2                               3  \\\n",
              "Image  ./train_images/d4ec04b8e-19.jpg  ./train_images/326c9c979-5.jpg   \n",
              "petId                        d4ec04b8e                       326c9c979   \n",
              "\n",
              "                                     4  \n",
              "Image  ./train_images/c670993fc-14.jpg  \n",
              "petId                        c670993fc  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "z_K1sCTIP8m1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "outputId": "4f5271b1-fc61-4bf1-f9d1-cdc4c23550d5"
      },
      "cell_type": "code",
      "source": [
        "df_tab.head().transpose()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Type</th>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>Nibble</td>\n",
              "      <td>No Name Yet</td>\n",
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              "      <td>Miko</td>\n",
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              "      <td>299</td>\n",
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              "      <th>Breed2</th>\n",
              "      <td>0</td>\n",
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              "      <td>7</td>\n",
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              "      <th>MaturitySize</th>\n",
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              "      <th>FurLength</th>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>Vaccinated</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>Dewormed</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>Sterilized</th>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fee</th>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>41326</td>\n",
              "      <td>41401</td>\n",
              "      <td>41326</td>\n",
              "      <td>41401</td>\n",
              "      <td>41326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RescuerID</th>\n",
              "      <td>8480853f516546f6cf33aa88cd76c379</td>\n",
              "      <td>3082c7125d8fb66f7dd4bff4192c8b14</td>\n",
              "      <td>fa90fa5b1ee11c86938398b60abc32cb</td>\n",
              "      <td>9238e4f44c71a75282e62f7136c6b240</td>\n",
              "      <td>95481e953f8aed9ec3d16fc4509537e8</td>\n",
              "    </tr>\n",
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              "      <th>VideoAmt</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>Nibble is a 3+ month old ball of cuteness. He ...</td>\n",
              "      <td>I just found it alone yesterday near my apartm...</td>\n",
              "      <td>Their pregnant mother was dumped by her irresp...</td>\n",
              "      <td>Good guard dog, very alert, active, obedience ...</td>\n",
              "      <td>This handsome yet cute boy is up for adoption....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetID</th>\n",
              "      <td>86e1089a3</td>\n",
              "      <td>6296e909a</td>\n",
              "      <td>3422e4906</td>\n",
              "      <td>5842f1ff5</td>\n",
              "      <td>850a43f90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PhotoAmt</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdoptionSpeed</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                               0  \\\n",
              "Type                                                           2   \n",
              "Name                                                      Nibble   \n",
              "Age                                                            3   \n",
              "Breed1                                                       299   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         1   \n",
              "Color1                                                         1   \n",
              "Color2                                                         7   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   1   \n",
              "FurLength                                                      1   \n",
              "Vaccinated                                                     2   \n",
              "Dewormed                                                       2   \n",
              "Sterilized                                                     2   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                          100   \n",
              "State                                                      41326   \n",
              "RescuerID                       8480853f516546f6cf33aa88cd76c379   \n",
              "VideoAmt                                                       0   \n",
              "Description    Nibble is a 3+ month old ball of cuteness. He ...   \n",
              "PetID                                                  86e1089a3   \n",
              "PhotoAmt                                                       1   \n",
              "AdoptionSpeed                                                  2   \n",
              "\n",
              "                                                               1  \\\n",
              "Type                                                           2   \n",
              "Name                                                 No Name Yet   \n",
              "Age                                                            1   \n",
              "Breed1                                                       265   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         1   \n",
              "Color1                                                         1   \n",
              "Color2                                                         2   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   2   \n",
              "FurLength                                                      2   \n",
              "Vaccinated                                                     3   \n",
              "Dewormed                                                       3   \n",
              "Sterilized                                                     3   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                            0   \n",
              "State                                                      41401   \n",
              "RescuerID                       3082c7125d8fb66f7dd4bff4192c8b14   \n",
              "VideoAmt                                                       0   \n",
              "Description    I just found it alone yesterday near my apartm...   \n",
              "PetID                                                  6296e909a   \n",
              "PhotoAmt                                                       2   \n",
              "AdoptionSpeed                                                  0   \n",
              "\n",
              "                                                               2  \\\n",
              "Type                                                           1   \n",
              "Name                                                      Brisco   \n",
              "Age                                                            1   \n",
              "Breed1                                                       307   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         1   \n",
              "Color1                                                         2   \n",
              "Color2                                                         7   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   2   \n",
              "FurLength                                                      2   \n",
              "Vaccinated                                                     1   \n",
              "Dewormed                                                       1   \n",
              "Sterilized                                                     2   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                            0   \n",
              "State                                                      41326   \n",
              "RescuerID                       fa90fa5b1ee11c86938398b60abc32cb   \n",
              "VideoAmt                                                       0   \n",
              "Description    Their pregnant mother was dumped by her irresp...   \n",
              "PetID                                                  3422e4906   \n",
              "PhotoAmt                                                       7   \n",
              "AdoptionSpeed                                                  3   \n",
              "\n",
              "                                                               3  \\\n",
              "Type                                                           1   \n",
              "Name                                                        Miko   \n",
              "Age                                                            4   \n",
              "Breed1                                                       307   \n",
              "Breed2                                                         0   \n",
              "Gender                                                         2   \n",
              "Color1                                                         1   \n",
              "Color2                                                         2   \n",
              "Color3                                                         0   \n",
              "MaturitySize                                                   2   \n",
              "FurLength                                                      1   \n",
              "Vaccinated                                                     1   \n",
              "Dewormed                                                       1   \n",
              "Sterilized                                                     2   \n",
              "Health                                                         1   \n",
              "Quantity                                                       1   \n",
              "Fee                                                          150   \n",
              "State                                                      41401   \n",
              "RescuerID                       9238e4f44c71a75282e62f7136c6b240   \n",
              "VideoAmt                                                       0   \n",
              "Description    Good guard dog, very alert, active, obedience ...   \n",
              "PetID                                                  5842f1ff5   \n",
              "PhotoAmt                                                       8   \n",
              "AdoptionSpeed                                                  2   \n",
              "\n",
              "                                                               4  \n",
              "Type                                                           1  \n",
              "Name                                                      Hunter  \n",
              "Age                                                            1  \n",
              "Breed1                                                       307  \n",
              "Breed2                                                         0  \n",
              "Gender                                                         1  \n",
              "Color1                                                         1  \n",
              "Color2                                                         0  \n",
              "Color3                                                         0  \n",
              "MaturitySize                                                   2  \n",
              "FurLength                                                      1  \n",
              "Vaccinated                                                     2  \n",
              "Dewormed                                                       2  \n",
              "Sterilized                                                     2  \n",
              "Health                                                         1  \n",
              "Quantity                                                       1  \n",
              "Fee                                                            0  \n",
              "State                                                      41326  \n",
              "RescuerID                       95481e953f8aed9ec3d16fc4509537e8  \n",
              "VideoAmt                                                       0  \n",
              "Description    This handsome yet cute boy is up for adoption....  \n",
              "PetID                                                  850a43f90  \n",
              "PhotoAmt                                                       3  \n",
              "AdoptionSpeed                                                  2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "38MH-iIiP8m6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "5a18d9d1-84aa-4101-a08e-b1aee32a492b"
      },
      "cell_type": "code",
      "source": [
        "def getAdoptionSpeed(petId): return (df_tab.loc[df_tab['PetID'] == petId]['AdoptionSpeed']).item()\n",
        "\n",
        "petid = df_img.iloc[0].petId\n",
        "print(petid)\n",
        "getAdoptionSpeed(petid)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ce0294cec\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "HAxy1tegP8m-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_img['adoptionSpeed'] = df_img['petId'].map(getAdoptionSpeed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "9ycOHa-pP8nF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_img['name'] = df_img['Image']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "BWrdW9aIP8nI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_img['label'] = df_img['adoptionSpeed']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "EQkpJ6hNP8nL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "del df_img['Image']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "tVuyq0aUP8nN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "del df_img['adoptionSpeed']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "9UZWdrYiP8nS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "del df_img['petId']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "4sCfp3Y9P8nU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e1a64b31-9d5a-4956-a5bf-49088be561de"
      },
      "cell_type": "code",
      "source": [
        "df_img.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>./train_images/ce0294cec-3.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>./train_images/2f4c6ada3-1.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>./train_images/d4ec04b8e-19.jpg</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>./train_images/326c9c979-5.jpg</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>./train_images/c670993fc-14.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              name  label\n",
              "0   ./train_images/ce0294cec-3.jpg      3\n",
              "1   ./train_images/2f4c6ada3-1.jpg      3\n",
              "2  ./train_images/d4ec04b8e-19.jpg      4\n",
              "3   ./train_images/326c9c979-5.jpg      3\n",
              "4  ./train_images/c670993fc-14.jpg      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "2O3N_9r7_4Bg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_label(path): \n",
        "  return getAdoptionSpeed(getIdFromImgPath(path))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d3OLWnQuMqPu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "70901a93-43e3-42a8-9e59-042a1076fb70"
      },
      "cell_type": "code",
      "source": [
        "img = open_image(path/df_img.loc[2421]['name'])\n",
        "img.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2kwvH0sWXppL/39lxBalp2DQbsVodwHV9SuMTuI5HoVBASkk+n+e2224jimKi\nXlVVhBIXCZ4fay2LXQWWLFkCMFP9jo6Oomkaa9euxTJMBo4cYvnSZdz87pvo6e7mlz//BR98/wfY\ntm0bExMTuK7L889vZv/+/XR1deH6FfoXdPPizmfZ+OxjbHz6Md7y1jdyZGA/e/ft4JzzTsMPHK69\n9g0owuDz//QVssleujrm4PtQqTc44cTlyChAILEtgyefeIwN6x/j/bfdwuFD+0EG6HrE0ZFhzjn3\nZFKpFCedFIf7bdu2kc4pLDthPsWuNGPjR+nr65kZGIyFNzaaGjcPGo0WrVaL4aFRnLZPqxFRKYdM\nTQYcPjjGvLmLabcCyhNt3JaJrhQoT7Q5dPAoveZCsmEPejvPc+v286MfPM7uvXXqrTwvbt8HmsFl\nV1+NbluceNpKdu7bw6YXXphxCseA+T8V7tVPf/rTn37JP/X/o33t63cQRZJGo4WUgIzHMEzTZqo6\nFeeojseRQwc558xVhEEbRQ9pN110Q8f1W5Qmy+zYNUCrHXD6aSczPj4+Uw07jsPw8DB93Xk2rF/P\nxRddgNNuoGuwefPT+F5rRkgNglwuR6PRQAgFRfPYv38f9913Hy+8sBnLNnl03cOMjAwxOTlOq13j\nzLNW89BDD3Do0BEq1RrXv+N6ZvX2M1EqU+zJ88O7v81kaZTqVJ1Kq0EYBpTLE2g6LFw0h2UnzOeF\nFzbxmU9/ga1btiEjg/17B7jgFa/k29/8PpGos23bDjZv3sLm57bhOhG+JxFCQ9MNWq0Wtp2g1XLQ\nNQvXDdCNWFJomQkyqQKVqSbZTJGx0UnarRaze5fz6qvfwK5d+6jWpjj11FUsS0re+843ErlN9u7d\nTyNKUaWD5w9VuPG2D/DMjr2se+45Xtizk+KsHhYsW0SoBCRDiee2sQxzmnFRKU9V8YP4mv4xeN/z\noY/82Tg5rp7UMGIBiOM4+F483qvr5nQXJ1bou67L+Pg49XqdVCqFUJhW2ItYfa9AEHg4TouNG2Nu\n0fd9Wq3WTGdndGyQgcH9vPo1V/C+W2/ihne/g1/9+qcoasjk5CTz58+fOR/bthkfH6c03uDI4XEy\n6QK+F/fxJybGWbRoAX7gUOjM8cSTDxFETSYmR+gsZtFUBd9rUejMUW+UWbFqefzASEFPd5HuriLd\nXZ3UqmXu+ubXef9tN3PVVZdSnhrlzDNXc/3115NKpfjyl7/MWGmML/7zv1GeaFLsnIOhp2i3Alwn\nQlWMP1FnIWNtrWnYTE6UCYIARZi4Dhh6GqRGtVplzqxFTJbH+Nqd/0I2rwEhO7bv5ay1C8n1BDy2\n4Rc0/THq/gSj9RGuu+nt/PYo+uvJAAAgAElEQVSBjTRdlX37DrFs6VL27tjCzs2baE6O43kemUxm\nZk7smP1Xk6N/rh1XFdSxcQxN1ac1mwqO45HN2vi+T+DXMbV4vORYh8ZIqaiKOl3l+9PdF5VIStLp\nFL7vk0qlGBsbo7u7m2KxyLp1v+Dbd90FKFx22WX81bWvYdnyxRQKCfywh5GREXRdn+kEmaaN04Yr\nL3sVe/Zu54QVK3lh69Mkk0kmJsdZteokXK/Nvff9IqaCNJfnNz/F+eechp1IABI0lVtuuYlf3v1j\nhPQJ/YCJyihB6JBJ67zumldx0cVn025U0a02r7v2Eh5bt4F0TuUd73pDXLFHOYYGKjSbbUwjhSIi\nTFPguQFELpEM8f1Y1JJK5pkqNxAq5HIZhNAJfIFtpZmYHCWTTbBh/XMsWz6fq151Db7vs6I+n0cf\n3sDt336MqS/8lPNOXUU+kaK/MI9KxeP8Ey6mKBZycOAAPckC2x/fwJknLOSJRx5m4Zw5NH1mRtL/\n2F7qvPT4ip61LELGHGC75ZJOx333aqVJNtVBu11HRi6Br2JoJrrSQkNHyPi0RSQQRBTyOY6O1qlU\nXFKpFANHjnDyqsUEYYN16x7klZddzeDgVk45ZTm9vSbDRwbYs20XC/pnsfacV3LfI78lWbAYHDqM\nHpnk7Tz/+A//zGVXXsm3vvtDxicnuP32f2Ljkw+w4YkHaE02kIrglhvex69/90tGSofIdGh84jPv\nIZvNMjVVYexomSULVlBrSQLDolgo0Gwe4PLLL2fj00+won8pRDYduTlsevpxHnnoQeoNhUZTUq3X\n2Lt/kLQuWblyFUIItm/bTTJlMTlRwQ88dEUlYSUI/AhdVfE9B0UJWL74ROqNKo12FS2fpOY10c2A\nRMagr78H3FVMlGpUnSc55YRLuP76q3jkoZ0snN1H3TVZc/qryGojDHIEJdKYdPfSNUvnta+5iV1b\ntvCbn93NvL5OBg/uZtaiMwhdZ6b4DSKfSPFjAfg0PaYoYqa4+3NNHM9/7NBRKM6Mwx6rsHVdRwiB\nqgk8r4WCjggUPv7X15M0W1hpExnocS88avL8i1t49LGdVOsRiqKRyWSQUYChhZy2+kTq9TG+eddP\nuO66qxCKR//cBfzgP3/PNZdfwSmnLOeN193EviN72HdgN3Nm9/Kh93+Qu793D72z5vDc5s28+nVv\noVyp8fTGx1m2ZB62DqXxBgBfvePLJDM2uUKGPXv38qEPvZe///tPMzY2xg++dzfSF7z2musodvSh\nmxpTlRJSBixYOJeR0SESCYtLL72YiclDVKoNDh4aw7JyjIyWKU/Wueiiczh08AiKonLv7x7gzW9+\nK48+ug5V0bETBrpuEIXxQF4QRFSrVTKZFI1mjVw+SUdHllqjhGYIXnnZWob3TfLRj36Udese4bKr\nV/HAY3eSSy7nza++nQef/BWRsEiGnQS1bbScKmsufBd2bglOq0YqobJl8zME7TqqgN/+9rfU2xMo\nUYjTasai9FCwZ/8BPDf6E4leFEW8OFj6s3FyXHPSY/mj4zgIIchmszNzRMe+HrO4oDJncp1j9NMx\nzWe83EGbYQBOPHElG596Fs+FG975bvrnLeJ7372bxYtO4vCeQ5x51rns3nuAZMrkoYceoNFocMUV\nVzJ/3gI+9IEPUqscRQZNhg/vp6/YyWtf/XqWLl7F/AUrWX7CSs44Zw1nn7uG/nmL2bRxC4EH551z\nCbbZQaFjFm958zv4m7/5JLquMzBwmMnJEq7j47oBR4fHaTY87vjXO9m48Vn27h6kpziXsaMlDu3b\nT9IyuXDNudi2zZIlS1ixYgVLly1GVVUWLFhANpeenrkKaDvNmUbH7Nmz0Q2VQqEjHq2pVLAMk3/+\n3OeZP2c+t//1p0jZDm98zRvwGwbppM7w4CE2Pvokz27YiCohCn3mzCqiKyGzevIE3ji5fMiTm37K\n/Y99h58/eBe/efT7nHPpiThOgzB0UVSAmNgXGC85To5ruA9CD8PUaDZd7EQKw9ToSnRSqVRwXS/u\niSsRCdOeFlv8QRAthCAScSuw3W4jVJ18vohp6py8agXvfc8N/P6+3+I6LRxH4bw1p3D/fY9y3XXv\nIJUq8PFPfZpyeZB8PksYubz//bfy79/4Bpde+Uq++x/f5qRVy/jSV76IaadZuGQJUaChqnF+nM5F\n1Bslbr31vQShy6tefTn3/v63XHLxVZx00goef+JRFi1cigzDeGzbsGm7LQBsK8msvn527HyRtWuv\npKc3w7lnn8PDDz1Bb9csurq6aDbbbN+6mc6+rnhXQCrL6tWrefjhB0kkUoRhRCJhYprxiLaiqOia\nSaVSJghdstksUoZ0FgrUqyXu/c29dHcXySXTCGHz3DPPsnv/c5RqDk5LJVqo0SwHLF28kp3PHSDZ\nlwWg3WqBOcqd3/k65cZ+xpqHOX/tWhJ2liNDG1A1gRDyjwol9b8UP/937LiCVFHA910SiXjxV7vd\nZKI0iWkZGIZFIpHAc0JkJGeU+UHoY6jTM+9+NJMexN7X5VO3f4rKVAk7YXDLLTfznW/9L9759jfy\nu/t/xsjICJryJO+++UO0WlVK1Uk2PfcAkWhx1euvZMkJy4miiO/86vt8/1c/YeHKJazf9BgPPvwA\n/37nt0CYtNoOm7c+yve+9z3GR8eYPXs2QwOHSaVS3HTTTRSLBQaHDjM8OMCZq1czNBgXXM1WC103\n0XWboaEhuro7yHcqLFu2mHVPrmfBggWEno+iCoQSkUmZDA8Pkst1ICU8++wmCp15XMenp6ePRrOC\n77uk02lGR0eJIolp2OimxqIl89m/Zzf1wCWbSfD0kxu582tfx1caePUimazJ2rXnsP/Fc/nxT79D\n4pW9LF5yOo9ueJ5qqU3fnDT7hg7xr/9+BwfGDpDMe8yen+PwmMMPfvRrVDL0z1sej+OEATA9VkM0\nI+45Zv/XS/WEEDMDb8f4TTthzYT+IAgIgmBGsvbHrz+ms/Q8b0YUbVoK3/v+f7D8hAW8/wPv4Stf\n/RK3f+bv+OWvfsqa887llltu4e3veieNZgs7mSCbT/PD/7yLlacso9Is8+jTj3HG2rPpWTKLhatW\n8rG/+Xs6Z3VRa5f5wX/+B08/8zCa4XLuWa/gPbfcymc/+3kWL16GImw0keCyy9ey9sIL+OXPfsqV\nV13O5s2b+f73f8DBgwfp6OjAtpPk8/l40FCFtjNFOquwYOlcRiYGyRbSoAmWnbCUkfERbr75Zmzb\nJpfLEUVx376zs5OBgcMzf7OmKQRBwMKFCzFMDYhQNYEkXodzwfkXcMGaC/A9j1B3iOwRIs2nUnZQ\nlYjLLrmY9U8/weYdz/Hbh3/Dgxvv58t3/TNPvLCOrQefZd/BKeoNg8HhGoI8SxacgSbn8lev/Siq\nahJFCpp6TH0WxMKVP+JHXwo7rp5UCInvu9NPm6RWqxBFAb29vQwPj7F69Sls2bwNIZXpsV8Dx21D\nKGNNZRTMcK35fJHRsUEqtRHe+a638d3v3MWXv3QH5dIot976PnbufoHBwSP09M5H1U32HdjDE0//\nnonSEPv272Tegj76V67ki9/4Cv/63X+jK91P0rQ476SVhLjce9/PWHpwKf/ypc+ga7NJJpOsPPEk\nXnP1Wzlv9aW4LYeR0QEkDu985zspjY0zOjrG6173OgzdolZr0NPTx+c+9zmWn7CAr935OS65/FQe\nWfd7lGQniqmgGLD5hWdpNJfypjdfxwsvvICmKQghp9ftKFQqFbq6umJNakeWiVI84nz06DCdnUWk\n5rN9+3bOO/tsNq5/ks3PP88Jy5Yj/YivfPPLZPMTdJqnceGZb6I8/gBnnbmC3zzyIodHd7N15AAp\nO8+b3nwhRw5v44H7t2CZ/QyURjhtYQebHirR39tkYkQnaVsEvsTQLVyngWEYyDBAiGgGnC9VTX58\np0VVE98TCEVDU3WcdoCmxyE9lU+y79AQViqL16yg6RFeIDG0BIrmY+kBBAZeSyOcpqS8UCNoh1x4\nyYWcf8ErGa1W8A1JR0LBayqcu/oqdDWFarlE/iibN/2Gz372e/SesIiy12KwPE6uOIeUlcBVPNy2\nw+HDR/HNJDYKu7YepJDqYcPWZznppBU8uf4+jhx6kc5Cnve97xauuOpN5DtyNJwmhwaP0FPsY/Rw\nDTVK0lsosHrVqZxz7vmguNz87vfw8b+9Ec9vUswtoT5cgZRBWu8kciXVyhjbdz7L2rWXMFWuE4Y+\nnhfh+7EqqdmYYsm8+Zx/ysk8+vDDZPN5dFtjydKFbNnyImevPJnDW7ezeukCdL3Os0/8kMWzk2TS\nc+nId7FvaB1GtpMdg8M8v2UDftXhY296E9X6KHufGSIM8/TY3ezaNozs6SKxbDVvveR0NmzYRDBV\nYlZHN7pQ8DwHRVNBUdGEwECnTbyn4Ngit/+uRz2uFFQmk8G2k4yPT2BbNkIJsWyV3t5ujgwOxIvC\npMIZp63kiovOJJ+2sFImuiqRYYDrRTy/bQ8/uOde7FSOvjmdfP4L/8hvfvtLbrzxRk4+8RRuuuU9\n3Hnnt9m8bQsLFiwinUwzXhti3/7tTJZHObhviL6lC0gW8py46mR+/KO7yaXSkLaZGBvHEJA2bB67\n/0GuvPiVPP/cc6h6PELtew69Xd3UqlPUahWuuPg6FixewMDwQR54+AGSZppnntjC0JExCknB/P6F\nXHzJZVSqk+zZv42Fy3pYv2EdnpqgPFknm+lC0zTaToPevg7cWoujR0e57LIr+MTHbyebzfGJj38S\nx/HY+swz6FLQW+yiOlXBC3x8GUEkqFYadHfNYejIID09HSiqj6aHhEqE5wUowsB1PToLfdSqdToK\nfQwPD9DVkySScQfQNFIYRmJ6cUaA4zZJJpPISCGRSHH06Cgrly7GdVtYthbvqfIkBw8eou3+gX46\n9nXzoZE/GyfHFaSFbAopBU47QDdUwtAn35HCNHUct0XL8ahXXIodJje+7Y0sWTgHqUlUIYmCkLbj\nsWP3Ib7zg19jJUyuv/HN1OtTSCm59tq/4vnntvDBD3+SUrlKR0cWicPz259htBQvVEil84yPDePK\nkKbvctKqlWx8cgOmqrHs9FNpN1sMHD5Iq16jVp7gwN59HDl8EOnFD1h1qowqBKtOWsn27dvx6gpm\nUmfOvNkMDg+QTmbY8vQ2IlcQek3OOecsvCBWDbWcBsOjBzlp5QmEhsrI0XGGhybp7u4mkTBoO3Vm\nFbu57bYPsG7dOtY/+TRvfevbKXR0ks/n+cgHPkBKMymNj0MUU3ZCUzGmI5JhpghCQYiPqoGqg63G\nYzjHumuO4xCFkMt3xelWo0Q2m6ZRd1BVg2QiS0QTz3OwbRvXdbDtBFLG1F9fdxHDVCiXx0mns9Mg\nPUz7j3jSY0T+C4f//E3PxxWkt934FkCJl10JQRR5JFI6Uobkc1labQfPcylPlsBtc+Ga85GWxFBU\n3LaDjFSGhkexc51IBEK08Lx4ft80EshIp9nwsFSbjkKSQDapNCbj3ad+xK6dB1h50hKshE0ilYxn\n6xUVXdVQrRQAigqGocX7p6bDW1qPc8IoiGfkAzeWFLYak6RSKVAkLadNFISogYpt2NRlI87f/Omp\nSlUBNfY01dIwHR2dtJpxtS6lxA9cQi0eFYnD5R+6OFJKFAmR62OoGoRxSPWJIGwRhYCi40cSM5nC\nDd14qUagIWWIUGT8VRBP36r2NK0np8duJFIKwgA0PQ7VUcTMMOEfRngiwsjF9VrTA3gqraZDGCkz\nAD3Gd3/sc//+Z+PkuOakXV0ZFEVDEC/FRQQYpkBRQbgRHdlOnMChkLPYueVF2s0Gk+UyvV19qIpG\nGEj6entJZnOUp6bIpjqBKF6VE6kYuk2U1bCM+AaEUsUQkslymWQyxZWXXkCjMkXCTlDIdaD/0ViG\n6yvTGs14zEPFQEaSrJ3GtuIpVU030TUdRbPx2h5dhU4GhoewkwlS6TSu46CqksBrk8qk4368lKiq\nTihDRKSgKhqaUFCR5LMpqpVqLKCRKkQSY3ozSDgNjJn5LtNEyggVgQRQBKpQUHQ93plKhAh8gqAx\nvX5Yo+W3MczYCfiBi2npqKrAbcWrG49ta7asBBKJVCMU3URGynSrM0IzJGEUxas2dZswkmgmIBWi\nSGDZNpEMZj7vWF7637HjClJTC1FVBUE8phyTwwG6qmKmLBzPRSNi7+EDXHH5ZYSez0hzPO48hSCm\n//jaVJlcNkPotqfn8kEREAQtkokckWwSSRVNT6CZGXpnpUhmTMLIw1QLcWUqJSGSSEiECradIHA9\nhKKhKiqKVEimkhi6TsWbRLM0QhlgJXUCNyDfm6HRbtA3fz6BhCAKMQwLNfCQUUTghWRSadquQxD5\nmKoB6vTy4I4c1VYT0wyotOvoSRvHcUkbNpoipr1SfI2OAdUXIYqpIIMIoQqEgCCKEDKJqoWYhoYR\n+jNDe822i2FaREREEoSqEYTx+4QaP5yq/od7I4REyJBI8RCKjqEahG0fiUREEaoqkVEYszOKjCko\nVBARqhrheR6RjEDyfzdIAzUiVHxUNSJETm9ujjtMepjGTqSZGBvjwXuf5LxzLsNTQvq7FxG4EUKT\ntKI6hBJdMRGuw2StTDqdROoawfRSCE/zSYs5tIMyhu4QqhIrUSAkxEroRMJC1Q0czycKNQwUiAQi\nctAVBUVIAumh6jp+WEfXLQyp4LdcAimplafwfZ9J08TIxloEXY/v9rFteUEQkNU96i0HIVRkFCGF\nIPQdgiAgmdIJHIn0PTpzaUKvTTaVwPPi9eix11YIgmhmjY9o+kghUFR1WhMr420EmkcUhjiuE0v5\nZICMBOmkSRDE8sf4H2qI2BMrKh4eqqL9iYAZQCgCL9LQtP+HuzeNkSxLz/Oec87dY8/IyLW2rq27\np2e6p2eG4+FspDWmKIsUZVgwIRC2f0iWacEwDAiCAeqXf3iBYdCA/cewQduSKRuwBUGkoA0cc7iB\nHHI4+0xPL9W1ZeUekbHe/d5zjn/cyOxuELDgaQqF4QUKVShUREVGfHHPOd/3vs+rqIoUX0nSpKQu\nGq+Z29EEoXflEnCUpS5LilQQBG2saPbK9Yd0kD5fPqkGW2scV+IqCZXBkwqRltioxGDwIvj3/9pf\nZTY/pdPrIo1E67Ix2yGpTIUTupS6xpMCTyp0WdNptxC+waMhgwgtyOKMi9mcrFWiTcFoq03oFqA8\nHLduzGRK4SiBUm3SNEXn5ZWA2ndcwjC8gjxora+GC1pr8uSoIfs5EWmaNuqkqsJ3XRaLAmstgR8R\nBi3m8zme5xOGEZPJCUEQXTkUyqIkXqV4fnS1J83zxoWg9TpCyIqr/x/ZfCFQEuE6f2Li0zxOXz3P\npd3jEsJ26d2/vEv7vn/1WCXsFUDOmkYWKYTb+Jnq9WNM81pQAum4ONZgqCmrpngrXX24OvlQj/6Q\nl9QahYCipCzL5tBBc/rMquaUHg5C3NYG1AVJekoWC3qdNr5SZFnG4eEx12/cRWtLJ/DxlUNRCWTp\nMgi3WK0Scm+K77qoGkRZY/0EKQWzyYqZbsTOuipxXRfpOkihSNLJexOdquLk9BTX9el2u1dElc3N\nTdI0vToxD9o+q9XqA/bjXq9HGAqOz5ZoU/Hw4R/jeR79fv+K2uf7CilnzGYLut0+Ukpm0wWlztjb\n20MIQbfbvSL/oRSrRcbJyQmYmjD0UY5gPp+TZSlRFF25TF3XpdVqXWkclFKMRqMPuEwvU2Da7UaP\nm2fNnU8IQWFK8rTJjVKINfwNagxFWiHXIh9bW4qqaoqdRpC9ipu8qkubzw97PdfT/b/3859tuO9l\nRRzHjIaNuCQKAmg3B40oiogCj+2NNjf39xAyapYez8H3fX7l7/4qOB67e9eIkwuyJCcIWvgqpCw0\nGEUYFqzmBUo45MWS0q4wUmFsiC06uA7oMqOqKmarJZ4f4rQcjK5QVlMVJY7nMl8meGGEoFHqX1qs\nL+9EeSqu0JRJklx5gOq6ptVSV7JEz3OI45iNYRNEIYRdF3aLLM3XP3ebUjeSQK2btlVdmfW/FzjW\nod/vEgY+g40OrhRoU2GQH5j4XN5BkyQhDEPyPL86efu+f/X6Los1juMPGOuU00gBGxxlRhAEV7lN\nOq9QQhI4jb3aCfwm20A3I+vVatV0PdKUf/4HD37oOnmud9KvPX7KdDpla2OIqTXJO49QUjIer/Bk\nSFbUDdqx1+ZL/9prTA4XvPqZ12i3WtRFs7+7cf0mbzw+5JO37/HNBznffusBL959mc9/8sf4G3/t\nb3Bj/waCCZ9+6SdI5yV5cc5P/eVPM48rnjzLsN4508U5t2/ssjkaUNkOtXB48liwuz3k67//23zl\nN36D/+zv/B3yx4cskoz/+r/8Lzg5OeHLX/4yDx8+pNbQ6fTZ/ahHq9WirmscZ5c8z6++aG999Yyq\ngrLMqXXMz//8v8N/+Iv/AUEQ8Mqrr3Hy7Ihf+qVf4smTp2yKRssQtUMuLi7Y3dlnPJ6CkoBFKkW3\n3WIyGTPoKzbwsNY0bSnZoNIvl24hXeq6pqolAR4IS5LEa6q1jzGaJM1ptVpo49Bqb7BarRqatOti\ntcUIB9cNqGuHtKgwWfPlfOnmLUxe4lqBFZChefMHb1BVnQ+0yz7s9VyLNC40XtTFCI+kiClrQeC6\nBL6LI0OEJ3Bcn8/8+OdwZM6tm7tXvh6tLRhB2GrjBAH/1X/zP9He8tjaHHH49JhfO/w1Xn35Y0xu\nnzObv814lmJzhyjoUlYVRirSUjGZj7m102NrZ8QqnjNZrLB+i//2l/8+L794lydvfpubN2/S6w3Y\n3Cr5W3/9P+Jv/e2/zauvvkpVWfavv9D43pOEeJmgq/dOslXVrBBWg+/38HyNX+fMFzP+6GvfQFvL\n5z//Od544wG//Mu/TJJka2pyQRi02d7ZwA/6nJ6eI0WI6/mYXCOVw2C4tY6Z1FgjQEqsrTG6UYxh\nwWizjhVqxs4CCVYgkEihwAqsAV0byqIi8MMmWFJbhAAtGgiZEQohHYoKDA7GXvY/G3me0BakQDZJ\nAleO0cs7+Ye9nutyv/viFlhL2w+pshyrDb5yWC6XdDaGlEaQlSX7mwP+9U++xEu391C9HoGFuihZ\nrRLeeXLIP/rN36awlroSuEoSuh6+UoSuR7fd4SR5Bze9jqoikviASbGiUqBtB8froPILlGnEu5UA\n64U4bkS/5XP65JiWC8IDo1xmccXuziaLxYKyrPB9jzQtCQIHUQUfsPk2AWTN4QcpQViMKUnShK2t\nAUm6YDDoMZ0u2dnZ4dnBEUJI2u0uQgim8xnGwPb2kNWyACtRyqOuDJglm/0uw40u13c2UUKDrcms\nvLJzvP+AF8dxIxlMksaot2ZJXYLPGix6H+CKnOK6Lr71kK6DsZYkz9DWUJuGIPPxezdxKoOqDEhB\nKjTfevMNtHmPrnK5dfjBwfSHrpPn24KaG+I45aJOrzbcnicpq4qz8QVCapRwWTxNeHH3Fvdvh4gi\nxrodglaHvM4xsmZ5ZsD2SKsYsBiTrfn5giw7IvQ9wmAG+gKlPMrcx0iPygiqdIbnepQV9Ds9qryk\nSCtCYlYrgxMMmBUlQit818MxOeeTCsfpIh1LWVv8sIuxlkJXOFbiuk1WlClL4jxDWIhs43z1wgi/\nG6FriZRtxuMM120TT1M23JDXPvE6D5884dHxKU67QVUuU0MQtLAa0jjBcTxC18dRgrLMyeoUSYVy\nLJ7xoKzQBkxdAT5lXiJtky9w2bO8/BJVVfWBfNdL3e4lGE2LHF/5IBWVyREoiqJ5ntIKKlPT8tbA\nNKNQeEjUVUaONR9+yX+uRZokGbpukiuqukAql7rWTeqKzJuRnBFIFH7QvDmuhE7oonVNELgYW2Ap\nyYoZNeUasCsxZCRZguM4xElKr9uiLnOUY9AUaFMRRh2CyGE2W7G1vcH5+BzHcUFotDCsigLrCoQn\n8ZRDVaygme9Q1+A4CmM0VQVSCgK3+eDLAhASfZXLDKZe4QYOhdGgwZUulS7xQwdVWP7SX/yLfOkn\nvsg/+xf/gtmqQ3d3k6PxU8bjJXkBpgYlBUZYshL8wKMsIQybPaepa1xXAHXDRNV6ndLXHLbk+1pT\nl8qkSxvOldNhXcCX2QBCNK0la0DI5gCVJjlVVV2tFpe/Lp9bCAF/ltyie/tD0qRAKR9dW7SpiOM5\njhtR6xTlWGxlGPVHJEnc9DuFWje0BVIZPF8QtmC4OcRrN3uhNE25dq2J0xmPx7x4+5N865tfI/I9\nymrFF7/4CY7Ozjg5HwMVr3z0FnGa0RcdXOUwn8+5fes2R6cnuL5Dux3hAGWWU6QJm6M+vV7vytev\nlOLx48e4VhC1W0jHoT3okhQJSZk28NtlwWAwoCpLfNdrsOlFycc//nG++NJrBK7DxeE7vHRvh9sv\nX+fXv/KbbG447O/usVzE+G7Azs4Ojx49YTZbshF0KfMMbWqKXKOEwEgXq0STg5otqbWm0DllWeNa\nAVKQF1XTu/X9hsm/7k5cInMu76aXFvI1r5iq0sSrFJDrhED9J/z1l3dh/T5G6WUv9sNcz1WZfzF7\nRm2WJOkEK1LSbMbetSGWnOGwTxB43LhxA4AkWVGWZSORW3+TweD7Lp1Oi1arIevN51P6/S5xvKSq\nChaLGQcHB82bXhVrKVxCksY4ruT+nY8yGl5nOi0pMkgWOZ51OD2asLWxS10JphcJy0WBrhwEEbZ2\ncUTEk4fHtMMNru3eRhGyv/MCL1x/ka2NfVp+H10qPNnFag8/7LNalgRuG11AEVfsDff52Z/6S9Tp\nkuX4BFHnFMmSeDHFVZLXXvkxXrj+Mp5qs711g8Ws5Ff/3v/N/bsfwXGcZqmuDUEQ4XkBjuPhBgGV\nae7gcZxiTXPyro1tJlOyyWsVSmIFaGs+EON+6YTI87xJbqkN1gqMbvCbjVwPWCuhLovw/fvw9191\nXf9oj0U7nRbLZULo96tSQnMAACAASURBVInjlLo2HD47xRjD4cGcMHJIF8eUScWrL93CdXw2BgPU\n5d3U8SgLy/nZAiUli3SG57mMzx6jlLxqsj9ePGW4sbZgeG3++Gtv44UupYav//E7GOnh+SGq1mx3\nu9y9vs1bj4+YPDmkFoK0rKiCCGE1EkU8X/D00RwhBJOzN65GlYvxMVo/AyxaGmprsJe8TuFidEUg\nz6nznNc/+iJ/5c//HBePjijLDGXAatgablFID0+8xT/+v77Czt4uR0fnHDxckBUlP/3n/2oDcrMF\n26MRed60k8o8RQhNVVvCsMXF/GituqrJ8wLlGJQjsfbSD1a/J0o21dXUrFo35C+XeymaOCBrG8t4\nmmQ4nt+kxayXdgFXS/7l3/1pnsefL8EkcxGmw9lJulba+OsRnEJJS5pkuNIj8DtUpV7L2EoQLko5\nVFWO67SQhOSZBRtQFBZBgNHNfquuLIoaKRUIj2ZSJ8kzgXJb1GaF5yuMrug48G//zJfY9OEv/Bs/\nyTe/+z1+/cu/Ra8VkJbV+jVanPeFjxW6CdgydY2iee2up6jzat0ELxoCtRegZEgdT/nCZz7JFz7z\nacrkAqsNjhdRFzn5KocCjmYnLOcpP/czP8eXv/KbtMI2yzQDK0niBOU4CNEEsYFYH3xAm4o8tXhe\ngECxSmI6nR5yLWSp1pI+Y5oCkkJgEMj37Scvi+v9TH9jDAbZKJ4c72pSprUGrdH2vbbX+4v0z4R9\nZDJeABD4EXlWoACLRWlLKSsQlkprtIay9jke51zfDSjzBZ2WR6UrXAGOn7PMM2xz5gEjUcLHUZKi\nKiiVpc5TPEDbGr/vk64ybFXj91sUcYJ0FLlT0+64BKsFcnXAx693ePVv/gLfefCY3/nGG9Qi4Gza\npPUpKdna6uC4kKWNvaPttIiiCIxle/se8XLFztYW+/v71FiqLMVmJccHB8zOz5GjTdzAp6NchLTU\nQUkpKnDhJ37qJ/n7//s/Zmt7xOPHD9nc2kDXlsWimeIoW2K9GmMrEl1SFCV5VlEsjvEil7SoCKI+\nkzQhiHySPEZVYaMFpdkueV6TMeDVFYoANBgtsUKSFxXKdSlsRmlyup0+y9kCh0YfaqoMZUqE0DjW\nAu9ZRSwfhJV92Ou5FmnQdkEbtrYGlHlJkZU4yiOKIsazM5Tj4Ds94llMVczphrBYzEhWM5Qa4QUh\nyvEJoza3N3ucTE7pt4eUuW7ow1KiARUETSR5XWGMRrmNX3+js8FFsWr0nMrB912WyyU3+n1yE6Ot\nZhnH7I5GfPZTn+DgfEpVF+vkPod7d2/TafksFlO0qdjs7jAcDtnd3mngDNMZ9+/eBRqevgP8H7/y\nv/HKyy/S67YJfBepRLPO08z7T54d8+DJAU9n8dXBbzgcUZVNX1iIJsspUIbAb6N1CcZSlzVZmhJ6\nEbqSeI7PdDrHCV1MrRu7tFzHDq37Q1WlkbLZc1aVBtnsWY3WNGcfiSPXI1LAc9bqrlpfjXjff/2r\nYJPCcy5SVInyXBwflqsl00XO9f0txtMjXMeh1fKgLpE2JUvOMHqG57fwfJ/aWKSVnE8XeFFEkuU4\nwmEymVDXEEURlhorK6pKYqSHIwQCtRZ1hJRFjSskQiqsEVRFzaNHT3BbETvXN+n0B9QixbcOx2cT\nep7gL3z+02TaEgQeN29eJ4wCNjf7IAybG/uNqEM1h5rdnS1W8wXtdhtdZHz1D77KZr/RChhdUeYJ\nSZKgtGY06FJmJXvbe3zzzSe4NuT27QHn5+fUtcbzIkaj9lU3waYJjmxUR0WWUWY5trZU1sFol7Iw\nhF5AbRrfE0bQjKEE1sir1GkjBZUWiNKA0+Stpk0PDW1B13mzL9UGU5dI6SCxjTDofdcHWlB/ytdz\nLdKq9ihzjdgKSDPL1s6I2XxFVVtKDYt0gWINhZCWr/zB7/HqvVe4c+8updGUKAptWC2WXEwzFAFC\ntUBAkjXCZ4yLpkQ7jYpdKLiYzAj9JpfUSI1nFcZYnMDjt3/vW3z2r/8CnY5HXdTsbI7I8pJXb9/g\n8OSY0/MDZDTkz33pJ3Fd1SylyQovjDBW43oOGEsYBZha02pF1HXFt/74Dzl6+gilDWmnha8EMnDp\ndyJ8BVhNnpdI3+fJu8+YFBLpQ5ZWeJ5PmjS2jSB0ybIE32rCVhe9hjIYqxHCUtaSJC3ww5D5fIYb\nuA3czG1eh0Q1+3XhYi2YSpNrhRQuWgm0rqiFxnF9jJA4rMUzeUq7HWEMlGUNfFB+96+ySJ8vn1R0\nEbbFgzcPKVPB+GRBsqypC0WZ1/SG21SlxIqA84uEj3/ys9x84RbSUSjXIwgC9rZGmCyj7YBvFVVZ\n4SgPW1uscTG1wHUDpLxsZkswkGdN/DeAMY0m0ljF5s4mbzx8hNUGJSSL2ZwiT3EFDDpt7t58gbPj\nI/7pr/8a0sJ8OkMIdeUBap7PfADAluc5SIXyXAaDjSskzXw+Z3Ix4513H3A2GVMUBRaJUC5B1Mb1\nBO1O82VKs0ZWN5836X5RLyKrM1QgUYHEjRxa/Ra1LJklF2hV4LYElckxwjBdzPEihXQlRkCcppS6\npqhLcBSrPOVseorfcXGC5nG1qIm6bcJOi1wX5LoCR2GkpUZ/gNd1eVC6EmW/r4f6YQv3+cIhKkuV\nFggMjb5HIzA4OChTIxZTPFuj8gw71exHG1gU0gtxjYG6ZHH6lLubgunEMikS2h7ERUarHbC/s8X5\nwRHzvAQvQEpFVpS0Ol1MBUVaYiOHojRIpciSjCTN0d+KeaEf0B8MCDsdJrM5frtHbV1mixntdpfz\n0zH/5J/8cz768dfobgzoOAGmzHFdFyWa0WhVlFR1jec77OzfpN3qQ17gquZU7YcuKIm2hlWeMhh0\nOL4Yk9uSi0VCfwPipMLz2kgpCUKXjc1NtneGHJw+ATStXotZMWWZNPAJXxiMjamlAEcTOS3mswta\nvRbLcoypQ4LWALymO+JHLq5ykcbQ6nco7AoR1ISdgNUyxQQBi1mMURYrNWkZs7m1yfL4uNl26Lr5\nstv3vpyu4/5LPvn/f9fzFT3bnNGgRbfT2GU9R1JkKVmWcW9nizxPufXKx3j01lN2NnxaeUE9GJKm\nKUEUsZyeURcxX/j0a9zc2+N7zx7z4PCcWa75wYM5r756g688e4jjh7iOj86bfPkkSfCdEIFA+R51\nUeF4LlqBqySZrui2W6yWSxarFBkEfPs732ceZ3R7A6RQhFGL8cWMR08OuB+1cUNDp90cyMRaHKyU\nolqDeYdbW0RBRLpYYIqKjY0+Rd1EMEolSJaSGsFrn3yd3/zatzhdnRCELp/77E/w9ltPOD2ZMJtd\nIJTP4yczNq4N8QPB5uaA09NTwq7PKl9i3Kwp6MBDa8FissRvt/HdDl4kqXKfNNF4yiMrVww2N/Gd\nFmfnh7i+Is0XlHWNMpog8pnMZ43qyVjKwrCzs8MqS9FizchXClM3MLnLSdWf9vV896ROja1qurVC\nljnGOAgFwpMcPjvnsz9+j+987Zu8dHdEslhR1BqT1qh2yGy1oOtH7G7d5A++/S2+dngBSM4mGQKX\nn/ix1/nq7/wRu9eucdcf4oUKYyvSImbbEZRVDUjM3OJsBiAFji8ZDTco8piVEri9NiIzPHn3Cd2o\nzXSxJM2WKBHQbXfY3t+l1evgqBpdx2B6YDWVLsnLaj0bd6m0bGb7UQBG4/adxuRXSWylMUlK5Ado\nBe8+fYvrd/tsvdSnqjIqd8y91ztsXK9YLCQCF9/3Ge73efr0Ke88fMitW7cZj8f0egOWywpdW2az\ngu3tHU6rA3AKhkOXZCUJ2i41BXkes7kboUXOwcUBQStgGpe0Wn0wCRZBf9htklKEYLmcI4Tk2ckB\nt27dIckLkiQhciXYCqUcZK1QVq25ULZxnApLbX+EJ05h2MZWJVVtMcLF90KCwKFeLtBIvvLNB4S+\n4g/fGjNot5hqiZ+viDp9oijArQy6tlgUz45OKG1FFLWRwuF7b32fIGjx5Nkhe5s+Tg4osxZhKNIk\npTfYoH8rosYymV4AikU8o9dq8/aDd3n15Y+BqLl/90WOxhNeuPECaVUzXq7QWjMYDAhaIRiLq5zm\njqIEeZbRaoVXwwRhwfGaJTBot4g8nyLLSe2Kui6pNGA1mzs7TOJZM8q9OEE48ipgzNpGtxCFPl7o\n8c4776xHmIY4jrl27RrvvvsIXefUtWY4HPL0yTPa7Q7LZXyVxhKnGf1+F+kIXnjhBtPplDvDO8Rx\nTFUV60NSjus51HVNWdaUZd4Ek7Ui/KBZIaIo+BNJI+/vjQr+5J9/2Ou5Fmm6bJa7UmmKMkNP51gB\nnU7Iqqxod/oUVUisE7K8oogCNrqSum70j9I6KCSrtMaokAzLMl4RBRF+LyJeLNESLopjVC2QDly7\nvken06asU5bJFDoZwhGIQU1dlnQ2+jx4+21MEOK2ukwnx1BlXNu9ydlihook/d1dut02nV6XytSN\nyt3YqyBa4D1itZRkaYVVEuEqrNGkukI4knCjh2qHxMsV7XbExuaIk+WEZbIkaPuE7RZVaXBdn7PT\nC/r9LtPlHDf06Xf7HD47pa5Bojh4ckTghYSdAUdHR7TDIb0Xtnh2cMj+znVmsxmpqdjf30abirou\n+eb3v86LL93h2aNn61hMSNOE3lr7MJ9PMVrgB40gZjye0OsOODs7pdPprlcKgakMSr13wldqLai2\nFmPtj3aR+oHLapVSuR7X79xkONxgPB4ThB7TxTlIWE0TdneHeHZGWcwIgn2enR9z49o18lWMtZbr\nL7xAu8xJ1qCsydk5npJ0ei3udTo4nuDp08dsDHvE2Rkb7RDjxrx07x7vTg5wrCCpYkLp8503v8FG\nexOn3UFGPkYKtDb84K036e1u09no44UBUStAr0/vjpRIQAlBK2xhbNWEakuFsQIlBLquKasKrWsk\novE9hRGOFLz8sddAaOJ8yc0XbuH9vkdhNRrB4fGE4XBEVhj0LMYPXGoDyvq0wi55npMsC4R1SeKU\neR4zHOywnCbNREl4LKYLHOkQ9kLGFxOqumggvKHDfDlHSkkcLwnDkJc/8hIPHrxNUdSMRh1cJ2I8\nPmM6rQnD5ssXrzJu376NiJstzaXQRCl1pQEwvE/K96NcpJ2uR3djh7TKmKZjJvEZjpSUxmO04bNK\nV2ze3eLs4JTdUY+W06hxtoZbvP3mW1zvbDMYDDh+43s8HJ9jsOzs7PD06TEv3N4BoVkVK6p8SWfT\nw6icsCcYzw7pboQ8ePQGwcYGWRWjKZglCfu3doncNlY7PBkfEbqKuFpRC0t3cwPZaeIhpeMgrMUY\nTV1WKF8iLPiuh1QuWldNG0s2p31dlNi6plp71pVSlHUjmXMdF6V8yqxguLfJz/7cX+Z/+T//Hnf2\nb7PqVpwfzwiDNtOjObdf2uPZk0N6rQF1qchTy9FqcjVTD9c91TzPMaZGOXKtmCpQsiLLGsljt92i\nLAS6tIxGQwC6vQ4PHz5ke3ubXi8FDGmaEwQRn/nMLVarBcdHZ/T7DSN1e/M6UjYFWNc12nwQIf9n\nwuPU7UsqNLU2vPLiy5RlTh6vOHp2iAq2iSKXWsy489o2cmlIS8PTJ6ds7W7y+kc/QTa+4PD4CC1L\nor5HMdF4SNqhy87+Dhubfb7x7W8AAhdBlRc4SjHs9JHCJ4xcdNJs8nutiMorCLoui4sZo9v3mdYr\nPv+xVzl9+IzlkwNES2IjhcKhNhphwfMbRnxdlNRrV6UrJGEYrp2YljzPCZSLG0SUXklWNnf8LM3I\n04zSV42FI3D4f/7B7/KDh2+zv3eH5XiFKBx63ghhfEYbIc/ePSaMPFZF00xv+5u4bhM73utts5hP\nqIscpQRVZfF8S5nlSAkdx2G0s8/FxQU2Fzh1yLXNO7z79E2uX7/e/L1tqCRxHBOEHta4bGxscHh4\nSKsV0u60SJIV1jaj4TRd0XMV1r6H6bnUpl794kdYmR+1PC6Wc4o65fj8oDkgVAWOKyitxO+2Cdse\nq3GOqhR+NGDYihgM+qyWS3RR0O22Ua6i3+pxfHRKEefcvnmLd999F/1Io0WFp9osFgvaYUiZafLV\nBa4MOTmacPvWHpPJEhlApyeI4xUGODg64Gd/6qfpDwf0ozajG9dZCUNsNFLIqw9DSkk7DBFCkOU5\n3W6bIAgQwq69RE1bSglFFIb4nkc1u8yfclmtVixXMVprvvl73+LxswOOxjP27+7jCocy16wWBe2W\nz2QyIwh9HOGS581zzy/GDIZDjJakSUFdGZS8jBZXuK7HcNhGKcV4+YQsa+Bl8/mS3Z09/vCrfwwe\nLJdvkqawv98hyzIsurG7qDYnJ2ck6YLZbE6n0+bllz/CbNaQW4AmrzVqQcmfuJO+//cf9nquRZpL\nRYXi9Vc+wcnJCSERhxcTVqsaoxN6dcD09AK/HdHxwZQp2rWsYvB9hYg8gjrixs4eR8sZsq0oZY2y\nLnVsGQxGZGnFeHlGvz9kfrYgyyo2N1pczKYMNzY5Ob7AC/uUaUWuBW3hc/+FPW52b9Gp2zidDRZm\nRVFobFHQdUOEKVGVxvN8ainRgcNWFLGchYROC9cJqGWOciVl0mQCVJXm97/3XZzekOR8DsmKV166\nznDU5vTZkjRLeOvdBwg/JGz3OHq2wnchjpc4rkNpa3audcmzilprPKtZTleAJF3l9LptSp3Q7Q8Q\nQtPf8EFKNkc9Hj9+hOs6iGrAchkThgrXjagLGHS2uH7rBd566wf0Qmi5Ix6884her8tksmRr1yAI\n8MUuJ+cnTM9i7tyynJ+ecXe/SxAEREikcnCcGtcT1Nq70qTW1nzI++hzHouenZziOS4nR6ccPTum\nyEp0CbYGx/HIVjmrZYauLXVlKPOK8eSUi4sznjx9xNe+9jVmswXLRcIf/P6b9HodOp0O89niKubw\n+PgMgOl0iuu67O3tUOQVuhYs5hm6No16XUNdGWwtOH52RiuKCDwfXdUIC2VeYGqNROC5Es9RrJKC\nQnh8460n/NY33qLfaVMVJbNJ07PNkpwiL0nTlK+//ZitO/c5mce0NoeMlwv8Xpd0nTY9GAzo9Xqs\nHVH0+z0uTpdEwRBdeSRxyfRiwWwyY7lcNqCKwEU5AqksSbJaH1Q0YeQxnpxz8PSCNClJE4jCAdo0\n7tEsq6hrmF6sSNOSxw8OiBcpthYcPD5cT+MqPvbyR8izHGsK5vNTtLb89E//OOdnz/jUp14FuHKD\nAh9A9bwXtqE/tK35uRaptXB+foExgo+8/BqrZU4r2mB35waR32a5yHjp7mtI47OYxSjlMtrqg9Dc\nvHmDz33uCww3Rvheh/29Ie12hBCC69dfwBjBC7fuUSUGx/HY3NxAKkOex2sCh6IqwfcbmEMQety+\ncZOX777Max/5FKONITujLVpBSOB6CGsJfZ/tzRG9VoTjOExmSx4+O+f7T8e4Gzf4ym/+BpOLM9I0\n5fDJCX/3V36VTqvP1//o65S5w/h0QdcNqZYJn3jlo/jKJXR96srQaXfpdNuUZc5iuWJycYoXdCly\n0LWD53Qp4oKwHdBqB3iRg9dyCNoKbVKKMmF/d5fNrR5SNYU+GAQcPD1BoNge3UKbnO3tEbs7+9y4\ndofFLGU2XVLlsNnfY9DZxndamArKzPCtr/+AVjhktHmDTnuTXrfNwwfHPHxwyle+/DUCP6Isa/Ks\nIM/LBqhWf3C5v7yjfpjr+c7urURYSbLM6bYseVpTFo2twfd9TC149+0nDHeGnI2PMJXGcUJ8ydoa\n0gTWZnnCKl5SmwRdOcxnKVK6vPGD73DnI/s8fHSEH0iMqah0jis7hGFImlRE7TbnFycIGXByfMj+\naISoGy7SJY3kUsyb5zlxHBMFDZLGcZd4juJjH3kZieZ8fMzodIONrW0ePj1gerHg4vyCjf6QJ8/O\nUPGCvEi5f22fT96/zywZU8fZ2tlp+OQnX0f4LptpzsHRGWlZUesmtbrb7ZDEM0ZbQy4uzhhubdDp\ntAhDn4vxhCTJODw64KWP3CNJ543PK1nS6bRx3Sal5f6LN3ny+IQig9OTMZ7n4aLI85LJ2Rmj3Q2q\nulFdNZ4mj/FxwvlheiWgWV4csn/t2jorSjSxkUikcKir5mcRyn4gNebDXs+1SPd39nn8+CnSCnSp\nqXODEg7tVpskT6lzjVGag6cnDLqN4l1Jn0G3h3Kax7Q7EYNBh93dBrvY7QzZGllWqxWeD8t4zLXb\nPdqdgDJ3WC5WrOYrXBniuh7n41P2r++ArbBVxWR8xk5vg82NIUZrfM+jdCpc5aCrmiJvkDQWzY+9\n/nF+/3vfZXL6mNJKPvn6qyRZzsV4wtMnh5ydnHNw8Iwf/OAH/Py/9Qtksibs+IxaHWRVUNclyyy+\ngjQ0PUfB9vaIyXxBp6VI4hIlFMfPHiF9SVEk3Lt/G+W1ePTwHa5d32P/xi6+G/Dd77zB2++8xfb2\niPHkhN3dLapKM5mMcRzJ9l7IKk5RQlEWYo2GrIgcn/v3X6TWKcPNfd544w2kULiOi8Gj1A1jVa87\nCllcszVsSNhKKaSxV/3RsqxRbv2Bpf/D7kmfa5E+ePCw8XKnOd/+1vfZGu2yWKwYny8YbLRwHI/V\nKseLHIQrG7mdcUjTkm43oq5LtC7QJmcVT2m3BhwcHLBcpjgO3HvxGmGnRUXAKr7g1Vde4dGjR3hO\nRZEopB+R1xln50dsDNp86tXXuLl9jRdvvUQYhmhtKIriSjghhKAsS1aZQ99vY8qUT7x8B8c1dBHk\nScpsEXN0NuXxu4+xRvGP/uGvcfP6Nd5854/4zJe+gBO6FMmSNFvxZHnKQpZsBh183yOOlyyXc5LJ\nOVIagrCiP+gxvcjoj3oMNnpk+ZTTs2dcu/URZpME6R4w6HRJVhlbw02W+ZKiKBiNNuj1W6RpzM32\nDnGcoo3GDyD0I0QnYLlcYrWl3Ql4dvgYRMXjJ08JgmYl63R6VFpTVs32IYq6LJcx09kpiIKXhi8R\neQGqqlHKRYj3kJKXyv0/DX3pc92T1nFEciEpVpbIDei1fYZ9H3SJqis2tyLu39mj7fiEnRYTscS0\nG4S2qDRWSJywS+T3WZ3kPPj+KfujF+i3erx87x6e8JmfZxy+c0KLLl/9rW9RLAQ3dq9jdMLevs+t\nvTafePk2P/W5z/PC/k22tnZo99vkRU1R1lS1wQowaKQjsKKmSCqWWU4paiQ1kR9Aq8XKCnJlEJHm\nfDHDyhZniwp/OMRxJXqZU00zskXB9CLBTWBUR3TbAVKEHDzLEKILGIbDgiBwqPSc4Zblxh3FYJRj\nZUzU1rz7vW/w8v0d+n4LW2oi32Nnr0ftzBFBzObuNk8Pz1llOTfu7tHaMMRpxdZewMc+eZ2NXcP9\nj+6xf3OL8+U5xgPtuHidEBl0iUvD+XzJv/mFn6GcadJFzvh0zCJJsMDWzR7T1QocheMpdL0mDCof\nnS7wbIkvDXW8wObph6qT5ysw6fpEUcDxySGtdoB1DHjQG7kELZ+kjKnimDwz2DKlE7TRdU0UBJg1\nzwhAWEsaQzgUzLMzSpZk2qcuc0pyRptDWlGPwM9ZLlIW88f0uhscH53Rinw6LY+LyYpusEG/1SV0\nfdK4Qf8EQUBdN3Q/4Mo9mRcprqcIQx/HcYiiiL29Pc7HgtVqgbCaNI1xPMU3v/l1/spP/6dczGfE\ncUycpFghm5k/sMpSjiaPOTj5HlEv5Pa1fRAVT5+eI/CIVw1YoixLBv3m99HeAKskq2XC/Rdv8847\nb/HWoxlBK6CuLRfTY3b3+0ynMx69e8T+/kucHx/RGw148vAJUihODx+zubnFj3/mo1SVpq4N77z9\ngP39LSbjKZPxBf/rP/xVRtsbIH2ilkupU7QpaHUV9axgOZ2B6yEcl7zWtHpt4qzFYrVCaIsKez/a\nfdKtaxvNsnABpc2ZrqbUuuT1T3+C6WTM2+9eUCxBGLh+bZd8lZMlKS0pkaLZD00XC1zlEHiwvd9E\nvdx4YZ84XhC1PPzSxVaS6XTBoN/wTx2nyS4tS8P2znWstgR+h9Fgk17UxrOCeg2htdZewWd93183\nyRuFUJKsMKbEdRVplhCogF67w6mnuH59n9XCUOqSra02qyRZm93WtmEBlW4MbReLGZP5EbfuDVjE\nC45OnzLojjC68Wz1ej3G5wustQ24N5ngtX2Wyzk7+ztYqdHCsrnZocx9+oOIql6yudkmXq5YTDOe\nvPtddrbbCKEIgwilFK++do0kLnh29BZ7e9d447vvMhx2ePzkAZ12h529IacXcwqdki5XGOtx784+\nSbzg7NlDNqN7CCM4PZ9isEzSnNkqJik9HNW06eoyw1MfbsF+rkU6Hj9jc3OT0WaLGzducHh4gO86\nPH38iCSdc22/i9yOOH+64M61mywnFwxfuoVYB65mdc1wtIn39kPu3dzm2UXJYNBncZGzXNYo26XO\na3wtqKuaIAooVyWZzhkMFJ51OX5yyidee5UbuzdQRuKgyJKUxZrgDHxAktZqta4UTmkaM5lMKMqc\nXq9DWRa4gWBruEmexeSZZu/aDr/4i/8uVRpjEBRZRlGVeEGE63tE7RbzbEmv32Z+/hRBzka3RzzN\nKVcVslJQCEI3whjDg+8/xhgYyxW7e0NMWdBvD3ntlTs4ruA7f3jEZquLNYY7e/t87w/fJPS2uLNz\niyQ9Ji5jtrYHzGZjzpfPiOOUre0ek6MDXrm3xfRiwXa/2e8P2h32+jdI4iV+ewNfQTiL6UiH/c6I\nyeEZx2vLS6UtMvRwXcVQWZS0CFsRtGB/Z+ND1clzLdKe57M4PWO0ucn44IjrW9skScLZ6RndXkhL\nulgpefHuLfJkTrrUlGlOGDXz8rPxObfaPZSUnB2egTdgFs/oD7p4pUdylgCC3jDkeLYgcWZQ1lR5\nSSqWBEFEt9Ph1u4uo8GAbhRSVBq91lW+H8ZVVRV1XZNl2XqkqQiCgK3tEdPpBK0j6rxkFacYW/Kf\n/Md/k//+f/gVcZdnpgAAIABJREFU/tyXvkheJORJQlHW5HneIM/X1Lq6rtnsDcmrOYPWiMDtspxm\n6CSj47fIlyllnNPeiBCO4Pa16xRFwXR+QcfxkbXl8MFjut0WYa/NR+/cp6wSsmXN4zee0HUjXn/1\n41zfv4fnaZSjkcpibTOaHQ5HaNOMSuNVkySdpjnYZvQri0ZA8+b3vsezx4+oPIWVktpC1AoJwxba\nKoSSaCxWGAIqJAKjLa5obD4f5nqufNL/+b/7z68w2a7r4/s+R0dH5HnOtf0trFtjjIswPp3A8ru/\n/U+5e/M+WzubaDTaGsKoy+/+zlf5+Kuv44XN9iEIvCuEjFIKSwOz9TwPo5tpVpqmWCtwPUMYRs2H\nYpsGdJIVWN2c6rvdLmVZXqERG6MZhGHIcrWgKDKs1aRpiqcDLCWVyZEyQtICp0KonCwuCPwI4Sik\nchpL9TqkwbcOy2xFq9/C931Ojk5o+QG+17Dso6iJT8yyrImeMYZ+N1pzmDS1Lq8AYvEqpd3uIIXH\narViY9jHcSQX03PypKbdCXHdBheulEtZaGbThCzLWK7mlGXOfHFxFQ1eVRpHKqRVjE8nVHndhMNZ\nKJwEKXyk8HGExFcCJSG15ZXfKc9zvvjFL/I//oN/9kPXyfP1OBnLaLDBxWTGZDVmc3OLvVGD8dZ5\nTpmkCDdA1yWelbieZDwes4zn3H/5PrpuAGZ1WTWqI50ROgpTptRrAcj4bIoXNHvJbBmvle4hnmog\nsZOLZxwfpnhBi6jVIWr3qY1Gr/ejruteFfvl8u84TuPstJZ+v08cL5ughqVmFa/oD9v4bp+8ADcI\nSbKcKIrotHtURmNZK4iKBgp2djalsnD47KxRv3sOB4dPm/3qxQWDwYAwDOn1elf6VaUt7TBs7N71\ne1ynlh8SBC2SVc32sIcfSJJ0yqAbUjoKIQyL+QXzyQXLRUoUddjo7zPcHsH2Hq4nyfJVk68lLWld\nomuLEj5VacgTzdOnBxwdnjA5+Q5ZusRUEqsNHuv8rJbLaDRia38PzwtY1er/uxD+JddzLdLf+/of\n4Xke7XabmzdvsihWnByfrWMJVeM78mC0uUGSL3HdLmVyzkfuf5Z4EqO9kqgdYqSitC6uMpTr6U2n\n06GqKoJWi8PTsw/EwVw8fcrGxgZBENCN+mzv3LwKOJCiCe+a13qtXmp8rBiLEhLHbXxYUkp6fr8p\neq+N67rUboWKmgNXnM0b5GNco1REOAjwwhayrqkrTVXrq0NZoUuSdIUSBqs1nupw/94tWkG3YUut\nuUvv34Kcjk+YrxZorZnP51eE6ajd3H0ve5WXROcoinACheP4bHduUFUVW3treV2Vk1YpZVlhVgbP\nDTCmWVWk0yy0WbkCIGg73Ht5h9v3R3zRfgprLUVRXHU9rLWkZXxld47jmCq9+FB18lyL9LOf+fHm\njVjHt3TbHUbDTQCKtCCrSmazGZPzMZQxh4eHXN9uUVQl7V6PWmaEUYOOKfMCHK4+1OVyecXevHv7\nzpXYwXEcru3tvyeMWM+WL12Ol8Gwl29+lmVXMNnLqBlLk9xxuV/t9XrNckuzDUiS5AOuySYGMkOY\nhvxhLEgLeZKSJynHx+fMZjOkNbQ7EVubI0K/RRhWaN1sH1ar1QcADN3IxwSNb+rG3vYVENfxGqju\n++/81lrKsmzAaUJQVRVV1mhabV3jIJpAsLrEat2g05u9Dw2TrPFZaa3RRXk18nRVQy0MWgFl+V7q\nSdcEV+934ocfuk6eL458DVC4fEPzPCdJkqY/6QZ4TrNs+J5DGUtCP6DV6dDu90jTAhlYKl1fvfnG\n6KsP5pL8JqUkWS0aLYAxlOumcwONcNcfWoEQdq2PNMh1eNflh3F597qMiPFCB4RFOYrIDZufA4Pr\nOnieR1U1H2S73V7z6CWBCtYQieZLcZnwUVUVUigEDmVREFPgqZgsLZCqURDVdU23273afjiOgykL\nPN9bk63fQ4kLzDosTTScUmvRdY3AYE2NkBLPVUjRAMtk6CMbtHZTZFZSVY2WtCgqhHKu9peOVDhS\nEfoBQgjqsqCqCqqiWXWq9ftV5O8xSf/f9r6lx7YlOeuLzFyPvXbtepznvdfu2+62cbca2TQWCCQL\nxMACDywh8RgwQjBhAANmMGMGYwb8Bdv8CyQGCCHRCGQaudvd7X7cc+551Wu/1soXg8jIzLWr2tin\nQHUOqpRKVbX3XmuvlSsyMuKLiC/i/4X4/b0K6ZdfvMBut5s1xlqtVmxnRQsdI3Tq84QQ8ezJEzz7\nxWewCFBtg7bV2G43MEphv9mCFPNrcukEa8eu69D3HaZphHMuA/Ncj6PSZzgOvd1yFv00jRjHkVvE\nVB00ZOJ9sinbtk2Cnvo2EDFleqNxfX0JrZl/yVr2np232KzX2G53cJPHdr3BuNsnLWgwjlsY02K/\nHzHZPRZxiaZtoQ23vTFNg6vra67WbAwWi0Vu6yjcTsZwj6jLy0sQET777LNsJkhvKQk+cKx9wm59\nja5bcPPblNlUmxbS5CEE7hkq87taDlitVhhHvr/tdgvvPVZDmx28mk7yfce9CumTR485+7ttZxEd\n5xzs3mK93XCs/PICrbLw1mKxHJifUxGUdnkCLy4u8PgJOxZt22ahd86hTdqH+rINERFi0t7yP4BC\nBAug7/ssAADytg9bqiKlEI05Sn3uMif9RWNkwW80k6JRiBi6Hm+u32Hoe+z7Ht9/8yMmEUt0PUxw\nC0RS2O7HVNhGWGjDGfBghmYbAQ3C6LmHU4wRNDlcr7d48fIV26Spq60sKtG8AKBNi36xxNGStfT5\n+SU2+xHDcAREQhsjxmRyLRaLfG9KKZBWOL9cw/vLvCNxvVYHN07oFjx3ec7uMO5VSEU4rGUed5lE\nIuLOa10LFzyePn6EhiZcvn2Fpu+gdYMwFTbii3fn+PwXfiULk7Xs7csW3xqGamQ9t0anrS0AxCFO\nIspeMqnIzbeSLSaes3j5fpq4Xt6OICLu7wlg3O8xTRNHphqDzZadDeccGj1kLbTZbNlOTckry6MG\n6yuLo9USbWtwetpDaQdFHfquz3N1cb7JDsrl+gLHx8cYhgHPnj3LWi5aj+H4DN88e5Jtcpln5xwc\nCNPE98VCFKHAFbGmZ+1qA3GfLABBEc6ePuF5aUzm11dKwVueb+l/ym2DJlyev+Z5b1usVquPmwuq\nUR3gbe51FH2EDxGN6UAh4vLda6xWKyB47L3H5FsYUgjW4WhYIoBtqdPHS078GLl5q2hXyQVdr6fs\ngYpdt91usVgs8kQuFgsYndrIeA2i0qudW5zzdm/diDa2CDEi+IgYA0Ki0qGgESYAWiEE4Kg/5t6j\nk8O+AXTbIkwT2kUPN07w3mKzucbR0EHBo20jmgboB4Xd5LH58icZ5jo6OkLbdbBWJSz4WbZPry+u\nZiXFADIaIEMcVNH6sh3HGKENFxYGBLjIbchJsY0bnYJpGt7x+o6fEwHRO+x3TEzxk5/8BLvdDo8f\nP8azZ8/wS7/69Ywpy3fcZdyzJrXoOqYy5K0TcM5iHHdYdAOOT09SQVvE6vgYl1dX/BCIqbInt8Uw\nMLueNCIQQby4uKga0HIocxg4Zm2MwdnZWe6ZKR3tpMpRKQWqMFFt2LbkTPN4w+Gr+eZFa0hLcbGD\nyXsQJSFJ8Iyco+8GGM1QnNYai8UCpyen+f86u70xClo1MKbJ7W3EtAghgBBnHekEmpL3pZxDtm1B\nMerGDCLozGAy5eOcc/jud7/LRY1HR3j86FOcnJzgW9/6Vj7fZrPJ/aJqlsG7jPstxNvvs00nQ7aS\n8/VF6stu8er1S2y37PV/89efgKJH2/YIMDlvMYSQOxmHEBgV6DosFgus15wqJg9VKj1LFxMUWys9\nKJkYETq5xhAcxn0Jm4YQMkx1fHwMgLXYdrvN6IIIMSMHihGA/YjlcpkqS/ncwzDMrlOpBvs9mxTS\n5Y4IaBqVHTW5dzlvDDYfDyAnyYgjaa003C2C2vVNgtu2mKYJL7/8AkdHR3j27BnevWOBe/78Oay1\n+NrXfhnTNGUfgr9/grMpf1TFVCFbykeU+og16Xf++3dARHj8mMkJZLUSEb7y1c+xOjlC1zU4fXSC\nED3evn2L3//d38M//kf/FOPeYppGhDghOI9F12fwuu8LTxFnMEmfIaaFEQ3K3+nSwuDmtKJRRMtw\np74IrVUWOAubr/Xq6gqrFfc1urq6yo6C9x673S7fKztqhcNTNC0LcllssmW3bQuKgOr6LKACkLPj\nErPjxltqwDSNUDQXiMVikbVazR2qtc6YcADDb8Mw5P5Ucg+np6cZBYgxou/7mT1bl9eUbPzi0cvu\ndJdxr0L6G3/pL2aYSG5wHNmbRSBsN9d4/WqdHqjG0XKJv/U3/ya8ddis19AmYtH3OFkdY7Ne49HT\nJznSIg6Y1hr7PQsL46gpkpSEbrkc8jZtjC7ogi3ayQebta73HsMwJKhq4iYLiU5StHPbttwy5/IS\ni1STv9/vocGZ/S71/JTUP60VvE+tDykiRofNZo8oUS/NySxaJU85BvgQ4PyUBVAcPMZ2KQP6m03I\ni63sBmX71ZpgtIZOLM8xerStgXOAcxNM02Zzh+c2wPsAUtzFRKcFFBPwz98DTJNNnx/vLCf3zKq3\nyEa8aD7O11TQETg7eQY8fYqm5ZaNr169wnLJW/piscDPvvgZfvUbX2dSiUBZeGq7cLfboW2bmYcp\nNmWMEZeXlzOnwhjDNp5yWXgFoJdjJBNKNAw35XL5d9/3mdte7D216KFVMWvkPkMIUNojRL6mtmkT\nwkDQ1XVzPJ1NIUUKgM4gPoCcqWWnbbZRkRa+UgpNWoBN0yAi5uO01iCFxNpXcekH1uzelxLlq6t1\nhvhC8Dl4IdlcSmkATP3YdYskuHeL2wP3LKTXu/UNm5DDjhHaE7bX17CWV2K/aNEojR9+/4/w9V/q\ncXx8iiePHuG/f+e/4eJdwI9/9BpRcRUn03ZfFMeiVWiaFm3b5gct2sW5NpsY+/0ePvXj3G5tjudb\nN8I5m6CykYsG0zHihIzjiK7rMI5jxhNryhmrGCMFgBgCGsVJ1FprLFcqO3AnJyeF9Gs/ZQ++aRpc\nX7Fmvrq6QjTcnW61WgEALi4usFwusVwuWfsn00BMAoArYGW+BZw3xkClhG7JEZCtWykFH6asTMZx\nzBCbtRZts8z29zAM+X7GaZ13sWm6W5oecM9CavyYNdI4TVgulxhTjHkCr3IbeFt9c77Dy5cv4UZC\ncD/ED3/wXfylb38Dv/qLX8G//w+/j0+eP0e7u8Ri4K7rJ4+WWO93aHuD8y9/htPTU7z+4hyPHj3C\nmzdv8Id/+If4yle+gkfPnmdhOjo6wjjyhK9OWOCWywGbTYRSfd6ij5bHOSI1jmM6jks8AOTzDcMw\n03RiW9YRIAl3eu/xB3/wB/jWt76VI13aULaz9/t90bxJ64uW3Gw2AL6a7U1ZhAIB5TyCQNkUEmHc\n7/dY9MuKXKzJmtM5h3EKmMZN3hG6tgXB4WjZwSkOYKwWCzhnsfMTootoFSHECG8jfLg7OcT91t2r\niD5pt9wXHhxRaStb9Yc//CH+6l/9y3j79i0oErzd4+tff47d+h10a/Cbf+M38fjRI0R1guFoCRs8\n2mGBlTqCaRt841d+GdvtFr/27b+A8/Nz/I//+QfYTSN+86//NcREq03EOZbSIz4kLqT1eo3PP/88\nP1ilFLp2kYWv6zqs1+us0cQWbppmBh8JRDUMQ3baxH7e7/cZHguhtPe2mzELnNi5cpwIn7SmFMer\nxkJFiIHUOrxiFpEFw6FVACDEKLxN6TONAqjLeQgAsjnDgQgNTUy/3jVtgZxcMY1q5OZ9x73bpLJi\na3uQiODsFuPEq/6rv/QLGKcNjlY93GQRuhbjbo2vfP45Xrz8Al/5+i/h3dvXWHSAagm9agEd0XQN\nLi4v0FCTt8nlconf+q3fYnIHY9D2BcucpDGDJnTtItleIcenJbIUfNFObPO2eWvLJkvCaodhyFtf\nvY0SEVarVYZzlFL45je/OYPkTLOYxb7l9RqXBZAXjwhGjXvK8fL98t01UhCjrzQpZl1F6g7OAIeK\nLy4ucHp6iv1oEQL3KTWJCjNWP0AJc99l3G/EKYXZrFXw3qFtORK02Wyw6EtWPNt6jNct+g4haPi2\nxdV2xPHpY2z2G3RHK0RSIKWg24JPPnv6FIo6BES0Pf9enRyzTamLsyaCKtpbHpRs2bKgAHCGUhIK\nriposncvQPZqxbxUsgCBImRyrIR/DxNYRJicn2bCMo7FUxZ7Wd6rX6//F9vZOQeKc5BfhHia9jMB\njpXGFdOknh/pO0qksgNY5z8UXBg3ru99xv1u91QyhICAEBxzYB4NePP6S7x58w6fffZZmnh+eNoA\nRjdouyEJGsEQZ5EjLBCUgYIGKQNFBgQFUGrKiiIADaPhmKZ9xkmXy0W6Dg94SiHVNU5PT2fXzdTd\nKoPsErvu+z6XKYtGZb77ollkG64TWmqNKBiubNnSMhy4SbRQ96iXcYhJ1oIT/c3XOUws5/GQ5rhy\nmjpCdbjYfMJIS5pgugYgmxNy7XcZ9yqktWcvP/Jwnz//NE0kt7HmvwkA98JsmwHO70FKwzQRpCKM\nWuZzaN1AKQ0FFtDbvmeaphyhkYeQ27wofnAnJyc3NEEdRZLjJKf1592j9z5BWeX7DudA7Nk6lFlv\nm7Wg11t5Xc16GIKUHUGEpv5c+Q5341rr84pQymITm7iey3rU5SyH773PuF8hhYXSCiAHpTVi9CBF\n8MFCk4JzPoHldcSoS1GOHY6W7IErGJysTrLTwcAzYAwLIkLBZIkAQoCdLBZ9D+cUg9NEcC5gseDz\nI0RM44i+5/aLxhhOHiaCsx5ap7onF6HJAAEIMc62SHmoxhhsxh2iIvz0xRd4/vw5AKBtW+ynlJyS\nFgcnUPMDhjIIqRWbVhqQ8KY2IJvSDV2ER4kkRU+zBaDBDpx3HkoL2K6zoHnv0epFnjvnHNqWTRCt\nNJqFyVUBNZzlHLc3IqLMj+USrizaVe7nroJ6vw1wofJPjAStmXjB2hFd3wLgFi2iZXhlujxZdd7m\ner2eAeTTNOWSiRhj7grSdV22cyWkJ3aoJFSI1hNKcXGYgKRtyMOnjChlVY7Rz7dagtIKRAohOHSN\nxnZ9hbOTFTSl0OgUoMCphABA0UMTL7AABtc5Ix6IwcFoDSUefWvSYtDpPlzSlj6D8gDjtyEwTNUt\n2MFzYc7TNI5T+jummLtPuQFxhiGLEyjsgkc9VyU8f/4cl5eX2W5erhih2SfhvStWer9CGg0QFZcT\nuwiCwhQCtpsJjepBZKCSRppG1k5NW7aZ6+trrFarjAkehv1qj1scFFnZEjmSvE7GRJfZCTg/P8+Z\n9/X5AKDvTdYOQsNzuPUJHgkkjaZabCjmJBLnHJdBa43oeGFIbVW9WCRAcHXF6XiCCOyu1/naLy4u\nsrcvfb0Exlqv19mhW52sctlKPQ8xaX5Z4BIpWywWWByf4vXr1/j000/xO7/zOzg/P88O1O/+/u/j\nyZMnePLkSUZO3r17h8WKAwpiAiml8LfvICb3rEkp/4g9BgDC9R6qyeNaId6m6nDlfr/H5eUlPvnk\nE7x58yZrVvmsjNevX2dtenx8nO0lSbgGkM9VZ1bVQiJO0Thts1PQti0uLi7ydigPer/fzxwNE9mx\nkvuQ65RIj9iMoq3k2iURXEK5Z2dnsNbi+vwCT548gbUWy36RHayu6/K1i8BLsosNFp99+jzDfRkh\n8CWlTu55GAYsl0uEdpHn5fz8PIdAnXP4u3//7+XFdnx8nDWpMjwP7969m+HC7y0l8a4pKncY/+Qf\n/PXsdMjkZo0EylpNhIOIYJpi2MsDFGdLVu5+v8/Yo0BAojnmKWYFs5SHrLXGd77zHfz6r/96xgjr\nECPbnCXzRzKGiAjrNXONvnnzBmdnZ7nmqOs6XJ6zpnny5MnseuUaa2GUshX5DoCFebvd5kVzeX6O\nZ8+e3ahuEE9dhFPQhdevX0MpXighMBGbKAaxI6VWqX4ODnMzRhw4ay3avlTMSpbUIXIhC/7v/MN/\n9d5ycq+a9Ktf/Uq2L2WCZJLaBOqP44jj4+NKyyKHDUVbNU2TS24B4PHjx/mBy2TXoUoAWRPXce6+\n73F5eYnf/u3fxunpaRZMzqRK5LnJEXr37l0ujZDsfjmPtRZHR0c5U6ppGtippLWJVpT/j5LJYq1F\niBrvzq+r4jd2lvYjc1ORivBBAbrDbgoIIUIpgg8KLmpucBYCYBo0bYd9sss344ST4yO0Ce/luHxg\nclxJqK6CK6FyAOuK2ZLrytWh8lwk7l8L6f8X3v3Rqs8O0dGqn5U+tIYFT7JsBDdsmi4Xk9X2H4BZ\nxKoGzOvJkkVRA9b1FinCLluUeMBElM0FZ4FhwZ1AhmHAuPfYbd/lcyqlcH21m3n6zWKRs6ZmMJFi\nO5y9eQXSGk1anO0wADGCQkCTFtM6VSAshgEOgDIGk+NSZWoaeFKA5jm0PgC6RVAN2mEF61jAjTGM\nICTglJpEehsV7GRzpC1ShEaBnOoFJppU5ohvZQ6dHWK47zvuOeLUZU1VY5hExHz6pBCCgtY8GVoT\nSBmQMvAV5MOYKAP6PgYoYmwVYItXm+KdhwhoxeXMAOBDRLQF5damRdOqXLQXIgGRssestYZpPbdm\nbIHJs/MVVUSjFjmpo2nZTNDg+9GWySOM4eLCSOXhRkxQVEo92gYw2mCyXO4NYm0XEdF2Gm03QHuu\ndo3eoyFujBgBuCpqVecCnJ2dgULKCfUhQccJVbAWnVJACFBEUN5DQ3DThK8G4iBJjNBKcb1WnmPu\nVMjyKVo0InhAq+bOcnLPEaebseUMiN9iKdca8tbkhciwSzk3l1uAwuGJcpc2wWVzODG9FwOfp7Z5\n8/cr5glQMQBECXslRPKACgieKbz5nkz6ygCpkyrXk357w084KoRkshBpUNwiJDoeAtJnkoaCQWrW\nzc3PZLcI8zh5yh1BRAQZAuChFSeA50CCIkSJQyhCQImQEYXUEjwtKGLm61h9Rp6FLFClPUABpO5e\nhAfcs5Bq1SStx6suRS9ZwKrPieASUaJ+YftSPpMxgqjTbwDBI6a+66QPI0Gc8SM4owghULYpiqpK\ndinRHqUU1OIM0Apu2sEoAkKEUYBTDlBIDl/kLCHNEBaBKWystVBGpwtOQhopCSAQEtMew1cB3vn8\nvckdARARMGUhDYHnxoWY5V5GnWjiEIvpkwQthADyBaQvSSdyrJ+B8zFfAVDY7COmSaJWwBTsbC7v\nOu6VM7+OY9c/wLwkV7YuWa0531Lr7HyIkIkNKbZTjVXWYb86KiTv1wkWkllUe7xKFcFtVIO2HWBM\nA8BAUcvtYkw32xXaroFpdD43F97xe+J5K22hjcPV9WuQmtD1AGgEooZWLbRqEYOCd0DwBESNGBWI\nTNLU/H/T9BhHD9Y9BuPoEQKbTMb0iEEjeAVngXHvE05toHUL5yJiVPAeiFFhmjxCIFgbYG0AoGFt\nQIwKMSp+bwpwNuLi/Bpts4DRHf7Xd7+HzjzBon2GVj9G3zxFo+5GonuvENS/+zf/bBY3r+PniCXm\nLIb71dUVjoZlxhglmiSrvOtZaGOQ87Aj5VEyfIB5DbpWbK8KjAPwFt81yLCVHCtwFqkOigwiAUoZ\nRFlgbsz1WJPdcdQmVUqq0CHKAogBpFWGyda7TcY4BToKIWC5fFrIwdLiyFhmYzLmKg7N9fU1xokL\nAvf7PbNQj2Oip1xjt9vh/Pwc+z0ToAnO+0ff/35WAiEEbDabnHElVaXOsenUdU1JmnFll5MI3Waz\nQTecoOs6XF1dZSXxX7/3k/eWk3smLPNQSiAL0aAJwKeQvXoRkFevXuHlaLnXOhHefPkKn376KRqt\nMe72cHaEcwExKHTtEsPQIlqFqAkgnXIfGbuMgeAj0A0SMVJYLPr8wBvFJSvr9RpHqVRaNCwC4MIE\nHzUmH3Cx2+P45Ax6beHchJEitAl49e4FnOd+TZt1i8s1x8B9CNiNe/z4xz/Gfr/Hu2uH9XqduZSs\ntcwhkFr0SBRIdgkA6BK2W+fhNk0DR3pW5DcMQ15ovuICmCYOiGz3I1ZHj3NJiHMOq6PH6BMlkUql\n04Kvtm2Lx48fcxXFdso7odDwrFYr/OjFjzEMQ671+qhj9/V2vtsVyCbGiMZQpqER8oZxHDE0Hb73\nve9Ba40f/OAHePr0KdbrNTabDfbTW2w2O4x7j8YcYViscH29w8buMmguP6KR7ARY6yB92wGuBfrN\nv/LnM9S12WzgvcfZ2Rm01njzxUtAafzky7f46ZdvsJkClG7Rrzf4jd/4Nr7xzV8B6QnLowY//sn3\nsFh0eP7Jt/EqgfzKaFxdrfGzn73g8O5+QNedIcYhP1wm2V1ns0MiN2ImuO0aJycn+OKLL0qStDHY\nuRQuPj7GbrfDm7ccBv21X/s1aPCxL1++xDe/8TlCCDg7O0NwjAHvdrsc0JB8hbbVOUAimLBkkOmz\nZrZwHp09w+XlJb72y7+YAx21ufS+4163+3/9L/45jDE5YUG2dgHiJXFDBmuzYp8Kg17bcjHdz758\nlbaxEZ999hmcn7BarWAQMAxDnmBxvLz3uT5Jtn8B33/0v/5LNitOTk5mob0vXr6AjwqX5xMCCD5w\nMsannxxjmvYYuh5tZ7BaHiG4xEJt2STxiDhfX+Ht+TmefPKMrylyFGzRtVDgnUQrwEV+uAKU1yHf\nptVZi9W1TBolFFtH2pRSQEr0qLHjGCNc3M+Qlhpf9rFwdh1WAATrboSWa9tehnMO//Lf/t57y8m9\natKzY84yWrQLfPKUE4sFMCcqNyvCw5ND4HIHh/20gYoez58wucSj02WOeqyOl1DJ0zZmzOUiADKo\nLlxQdeyeiIklvvi+QUz22cW7dwiBmUrOzs5gpz0UNTAqAlDwFGFag840UBG4urjGbnONEBwenZ1y\nOFN16Ic6RN/wAAAfu0lEQVQBJ2en2O52oEBoVQvyBG0CNAgmedKGCAoKOj2dxgDUGI4seQ+tCEoA\ndFJoDNuMmgIngBMhRocwTTBKQSFCIYIaEU6CtR5SNgKlEBK8BQCU/o9EaMhARZWFD0ioQAwYx1TU\nZwMa3WZziBDyueo82Pcd9yqkrQ4pKcHBTxsWSNjkxOw4AkOEcWuTHZk88sCTcLQgGNPhdGXw9u1b\nRGvRaM3lzzQiRg9DBgoe0+4avgq7GoqAHxGsR0gZQ24cM0vzauDSj6EbcHl5CWstTlfM5jE0HYIn\nhEbBg9B0BloTgotYX22gwawsXKIW0KwaENgh3O12aLXB6ZNjqBDRdS1i5KqE6CI0ATFEKDIIITmO\n8DCaISlSbApxSh6BbyeiTazPKoSMfkyTy6l3wQao1oAU4PwE6GRfE6BUWzDOBD95LxhuiTDV4U7v\nueGDHQvFZg4Lu33WtqJ97zLunbCMWTMkSZkTn6UCUjRoXUQW4RHhE84HPHv+Cd6+e82gNgiKOPXM\n2RFKEUhrUBxAUUGBV32OHKkW0UdEr+GthYLBkCAiZV6DImsC03ZQpoHzHlxdoRCJAE3QkEx9BR8A\n3RhEHxA8EBATgRgApeEBGGWgSGE/ObRQCH5E15e+nDwXfL9GM45MUBC0XSuDGAClzSz0G2OKrRvi\nxhQ+RbUQmctVaUQShhafIk8JkrNTWrwK1gllTgLpA0fbQJrLlFM5SUiKwoWI6DiHNRKXMJNqELzn\ncuZA8OEj1qTK8BYhUZT1dp9gJQ+fcMFIHSZbSLe0ZqoXgFPuNluPiA7rzRoqarRkwPkVihn79h5Q\nbBfa0WfupQCCC2yfWgeQaqEUYP2I4APQdRitxd57RM0CFgAEIjjXsvgpYUPh9zRFjIl1b/SA0oCK\nCcNN6Wvee1AIUEHBxsSnn7RWk2jEtVJoGg2lkb13rQswz5opzIQ0J4DQPHYuf4cQECLlpBXvC1ue\nR7FrxY6VY7wrPetreIwDHjGHXev3nQ35mI8+dn9xxSSzdanB2/OLFF1pbzXYrWUPlBt9HeHtu3ec\n0LHeoW+XsGnytSE0+0TZ00xwgckc9lPM0A1QkiJksqUc2CedvZvGnIAskx6tYggrKPjooBWhgQJR\nxD7x5RsFKM81Wbpy+EKO1RCMT6lyk0MMHloTWtOg0YlFj0o9lDhNWUipBDDkM0QE+HnW/ey6I1V/\nF+fIBj8TqBplsWFOxy6vxxgRXDlOPhNjhLPFAfvohfSnP/0RAI7i7HZc6xM8T8p+GjM+KmltRARP\n/FA+/fRT/Mf//J/SsTt2Tvo2C6AwPQNAn/JIJR+07/tsTripVGZaa3NpSUMN/Ohx+fYys5Xkh6tL\nUZv3Dtq1INUixhE6hgSAa4RUQOgDYFwABeZWIiIG9gUTNlIOrDABcEFB+YDWzNlEJC0xa0iVMpQi\noLVi7FOnUpAU1vSh2JrOB/jIplKIHHL13kMCruKQO2ezkDV6CU0hZ0UFKoLqQMnBjdBUBNu3ju3q\nAET3kQvp0dERpsnNGhN4Vxg85OEAZRWrxqAxBo/OzrC+vuYSECIMiwWUIi5SI0IK20Nrjb7v8oOt\nM9LloW+32wx/CT4oXVCOj4/zNibDp7i/LBwB0pmsq2TV19two83sf6BoQKjyujzoEAKiLzhy7V3L\ntnwIz4UQ4MJmFlmr0xJDLLuWnCfGmL9HRk0GIZ+Rv2VINFC0aH3d7DAqBARQXgLvP+5VSBnAT6ls\nWjjkS2a4rOYZ/BEjPv/8c/zxH/8xNptNDmfWWfQSLj0UcHm4kp3P0aOIp0+fJj4lZPBZSkwExK4F\nIoSSDC3v8d8q47x1QgofVJyHWoj4c3TjPQBwsbwmApFtxYqMYXZOhNn5ZSGEEFlklMnbPpBSEA/A\ndrkmosJVIAtIdjZOAiqLq6YhCp4zvhTmBMnvO+5VSN++fYsQhIAgrdZENagbUz3EohU60+FnP+Y4\nsCEGkqPz8CFCpdUdncdkHWLTgNqITfAZzK8Jaa21aE2D69R25qc//SmePn2acVSgsJvIqB+8LCRZ\nIEQly+gQG1R6LlC13Vc/SLlPnoySwnhY7CdCczgiOMbOn5VqXCYPVinjyzkHRSXxRq6p1vIyQlRZ\nS0sFRIaWUKoegJJ8PkAaubFzdddx791HADXfttIc+RhmmlTql549eYpXr17lqFH0AZq4Lp3ArBpK\nqcwyolPmEuOGXOYsAtu2bTb+nXN49OjRTAvLlio/edurNIgImbCw1KN+4PWWKhlYebuvPldnUEmd\n1+HrZe7SnNXzp+cLZCZ8VeK2HMeIwJw8Yzbvpp8tqvpvoyglxvSza7I+gKLU/dPHvd3zJOn8GwDb\nMMRYn0yoCOTJyUnuxlbXNuWCMlXOK0LNcegmO0oAcrqc1jrbrSI0OdMpvS8OV03i5SNmAlMypQpc\nA8wpb6Ka23PyfUqpsjKr97TWaE1zQ+vWOwswvw6AYS+5JqDY4DFGhDhfXHmRhHLOmSYHEGm+SEQQ\niQjBj1gMXb5fEWJZrBRKFOsu416FdLlcQlJalUoUMySzXEiylFK5E8d2s8EiceLXmg5gD7emysla\nQ5XSDNGo8rDcZGcCIDatTPyt+aiVrTgvUpsLTa3t9AFVz0zITdnu5X5kAcl3y25wCO0cmhYxsZlI\nal99jEmNx2rNG2OESRWmtZDm3YOKsEuebj5HLNW89bFEvjDspUZodxn3690PJzPNUY+rqwtcXV3h\n8ePHuQb8/Pwcu62D0S3azqBpDEMgmh961y5mkywCW0/6zAlKjRYkkaWOpoiQS8ZULaTSb1MyhkTg\nOKRaGqbN7dIwE8yZBx7nW62cM8RpdszcwZp3bJFxmDc7szHjgWklKETNZFYdd6ilo1KIpkBgREU4\neS6SOOkGMXrWpMrj1lqgP8O439h90liHWTMAcHZ2hkePHmWhkk4Z2jQpWYQdFQCpGRhlU6HWorVN\neWPSY5wJrfxdA90AZtt3jBEx9WMSeh557+TkJGs5qaUv43Z7NcaYQRrZgkVIQQXpOJyf+v+f53gd\nOnmIZZ6zTZ8WyeGoPfrb3qsXrVxDTuBBBKChFAcy7jrul478Vo3DQ14S2/Lt27cpk90k3NNnuEqE\ntDFSVVrS1URD1iUm8r01jFM/jPqaDtnoREhFEA8XWC309XYvQip2Wy1ICPHGOYgoQ0O3CWmtEf8k\nIZ1dUywCKwEMdtDm4zaNn+/9wCauvzObHiGZHVGD+SY/4mZj8oBrzxFAtg1jjDg/P8dqtcJut8PR\n0RGaNoKUTx5pKuCDyT6knEecHWC+lQK4ASnVo9YeNdRTP3Af55rjtgcKYGYXhlAaQRQmlDDTpPU1\nxMiFebxDEIIPM017qKBqHPk2TcqvzwU7e/kINzRjbXrUc3VYl1bPYXEkU8ILImKIuKt7/0Hwk9Z2\nmPxvDDtHT58+xQ9+8IPc5KppIwAHpThzyBjirKAqji2YntiTtRDdJpT19YhGqLXsoSYjmuOkt9lv\nMkp0CPn66i33tu22aP1ihtTcpUSECHtwTXOBObxHpVQu0z78XMRcK9YCX4/a+ayFWuZcjgmBy4FK\nC8mPOFWvZhM51ADCifTy5UsAyC0RQSOMaRFDgLXMpcm1Nz1i9Bl8F9rHWgveNur3DsN+9TXVAlHj\npH+SgIpGz6jAQWb7z7se+YzWRaAFGsu7jioLqr7uOlx6iFoIgH/jO3ETgrpt3mrSYNkVZFeqFU4I\nie8gY+Af8XYfKeUEpTo8H1NURRPapgMUQTcGw9ESLiRqx7AExQZARN/xA+EkYM7at9YDmeys5Gay\ngMyFBpg/yFrY6m2O6MBRqXDN2saM3s1sM0WJOEJxeqFKSbAx4YcUk75J0FXTNDl/VvJBfcKLJTdU\nKYJWGsoBKioEG9Bok+WAYACfFohcc4q8elVs5BmcFeX++Ho5VZRDqLbOWfAFuyYQSCsopEVCgPMl\nEVvmxMeYo4nvO+55uz/UPoXOpmkaXF1dwXvuriaeNKUkCWlnU0dvbtN88iDq8ufbnJSfpzVnVyfv\n/xynpUm1VmJuiEaSLb/YbHNtGlPWvncebbOAsCyDCoHvoVmidOK8YvyqXKOamyg/L1Wu3tIzxUOl\nDeXY+v5mgYEw50SoM/APYa6fN59/2nHv272M2puWLUy2bIkWKaWgElW3tRa73S7ze0qSyeH2K9tn\nPamH41DIfx7ikM97i6fN4Hu4oakPBfLW748aFFnzUST0SdhBFsJKFQ+jWE2b+jKp2aIJqaxEGEpQ\n5QxozBcnXxfnQMg2zfNVmzbz+zy0R2sSj/rccq2HSuF9xv2WNPu6H3vCN0nBaIPz83Mo0qmEQsM7\ndiZCDHlFS/VnndRRR6BqgauFRF6TY+W1Orokvw+FSxKXD89tbREoALNrmm2tVZqdcAA4S/k65N7Y\n7EkNag9oLAGAVMGARes1TQPTFJZn2ZFy95IqxbDr2pKuqDS22y2OjoZZ1pe1gEoObB39k1GnKrKz\na24IcQ39ve+4Z00q2eZFq7Ztl+q/PbQ26PshG+xKaSA1igXmOGstdOxItTdszcPtXCZY/q8TKA4n\ndib8wI1zy+v1ODQr6gco53LOQZNG2wq2y2ciMmjaJt/Psl+k98UxCrlDtDRCIyKE6GCGRbbDxc4N\nIXBdVXImJc1Ra43gQ+5IKLTtIqiTK9dbJ1zLPcmcCVJRO1yH/7/vuHdyCIGfaoaOzWYDpRTTYVcR\nKQGk5aZla601qDzE2saUBXATSrodhK+jNHJ+eUA5+VqVFLZ87lvusT5vfa11ONWokgcgvFGS+mdM\nO2vLKAnWhkLm6398uroRmJDr2+/3MMl8MGnn4UTwNqcjKnBu77BkxhFSETEGaKOhQrGjDxNnajOp\nro2q77V+Du877j1Vj4iqvu8al5eXqBk0RNvlmLpWeZuRUQPTQBF+STS5TZP+n7b7w1Bo0zTY7/cI\ngbVOTbQrx6tbYtT1d9cPTbbjxWIBN15DKYUh0fyw2RtgTOoqTYQu7QyCF+uwRd+p1P15xNDxZxF5\ny91sNtil5ryr1Yq5oRKNjwQUcp+CEPJnhmGYdd5bLAo1umjhXCZSVU8c7hC1Ivmot3tjREMCqSkM\nLi7ecVre6TGMSQZ9tNDpbx89dCMTwdCHDz7zHGVtSFKawUQGiooIEdixgKLEisfD1phfFKHm7PTJ\nOrQpb9LZxORXJXkQCJHsbCGUhZRwwwRLCcAt27PqBqjUNscogtQMEU1oTYtGE2IMTAjsdtDKQ6FB\nSwsY02GxWGCaJti9xWLFBYzDwJQ9u90Or169wuPHj6FdyJxOY6rAZQ2t0HUtfByBSLmKl1GUFipV\nqkp3lAiOIuW+UIYz/yXTyztK93wz5PxecnKno+84JFMe4MYFL168QNu23Jc9RDhbSkG04s8p3Wai\nAmFrllE7EqJZRXPV4UEZssplEmvzwehiq9ZOgDg+krpWb604ENCZFvU3w4lFwyheCqm+3UcO+5Jp\n4KBAqoFRGkcL5vvv2w6kR5BmthNqA370x3+EEAI+778BABhTrdjLl6/w7W9/m1vjoMFua9kOB2cq\nLRYDvOO5NrpH27bY7XbwzsGYDuPeYxxT4wldsp6yd48SQMmYsyrh6Pr3+44PIsGEiNPchP6QPdUu\nC4N4jkQEpUso7lBr1bhe3oIrr1/GzFOvMp3q0KOzN2uIxPSow611a5/bhFTGIcQ1F9aGt/ggtp6G\n6XtEDa4mcBZnxwNgOmyu17CeQHosjsxuxIvXb3F6egoXgWEY0A1LbLdbfPILBi9evcZqtcJXv/o1\n/OgnP4ZzHk3XYZkWotGUnCtgmhxOTx9hu92y/apadK2d2Zz1Qhdk43h1Vu4p2GxWiKlwJzm509F3\nHMJad3V1hc1mgxjjrAvdYdpcjBGtMuja0tmYBbBwmhJKMZxouUNbU7QgIm4kEx9CVHIdNWFZTRy2\n3+/z6123yLCO9x6LxSLjvt4W4rDaFq4dPkopgOM0QSkDkMXp8RnzVY0jiDSOjo7TYuW0QG2YleRX\n/9xf5i3fWrx+/TrbnYIlv3jxAj+bXmA78pbd9h1ssou3231GCWKMePPmTRVZYl/h+vo6l+SIPVrn\n3Ao3AQD4aXcjB/dOcnLnM9xhLJfLzMm53W5xenqK5XKZ+oAWT10cI4Y5/A3Y6DagXBp4yVYkPemB\nkjPQti1MM9eWOXMKhTQiRu5QUm/TNVwlgl2XmIQQ8jHTNGGbSqTlM2IPhhDh7Q5b7zE5XmQ+stPk\ndpclUhUikPhKG2NATZ+vW5qYdV2H64sd+lS5sN/v8ejRI8QYsGgbOAJMx1ym1jtoX+q42rbFmzcj\nQog4OzvJZtfl5TXbuMsniDGmVukRTduklj4EUMA42Rk+LeOuWhS4ZyF98eJFvqHVapVDoW/evEHw\nJguM9F4CgOMT9qzHccRut5vV0UvcexgGPHv2DOM4zgSn9toBnsB+0dzYmkMIaJt5C3Bxyg5t2Npu\nFa0tWqzuyGwq9EHelx2i7zy6vsVC99iNExbtEm3XYwXeVYLz3CfKOdg9b/P9caliFR7Q7XaL1fDZ\nDNN99+4dLi4u8dlnn2FvPUbH19TrBpfX1wjBYpw2mOwWT56eJlgqYr25wGS3GEfe6qfdlIMpTCzB\nHFMxRpACjCZ4nxrohibXNxm6m2cP3LOQ9i2zAw/DgE+efZpxO/boi7dea6dxHLHoe3StwXLoZzjk\ncWJk9p57avZdA637RIT2J2dC1faraLzDoYh9fVLiLHGPeD6Wf1wiYAvOwzQpUgSC9/P6Iedsbl7m\ng0N0BMSI3rRwqQbLGSZs6xqdiXy7toHpGuw3Uy4o3F4z28vTR5/kfqliK7Zti6997WuMf8LD7bew\nPkAZwy1xosKnnz3D+noD5xJFkW6wWCyx300gaCAqNKbLc+shwH0VwIiAdwFadaBeg7yCT/Vj+Jht\nUrHn6kapInDWzTWeaDVpPqaUwvHx8WxLru1Difcz/+i8S/BtkSAimnFp1p5+rYEZ1rr9XLUzV4Pc\nebs++E4h7xUmwcPFIpGiGFmD1QvIOZd3CqmclZbfYivWZsXx8THC1SWbIM7nxmb73YT19abCjR2I\nwg18uuaUkt2JiSbKLiXfKU6Xort59TLuVUjHBDYPw1CcGRSoR/6WhyM2peCCFxcXeYuV32JjOecy\nAzHRzShSPW4z8AXQvg1GUUpoapCLz+qHJV5wDXEZVZiZx3HEMAw5Uabrmtl3iMaKwSf7kuv261Cq\nwGziHNal2NvtNiMPAs6/fPkSU9Leynmst1tQur7JSe4tMAwtvIvZK6/zBuTa5Dl4f3vYU1phOjsP\nurzvuPcsKHGCdrsdb2cdQ09SmisRDdEwwzDMtCdQhFoEXTSqaN4YS1RJBKAe9STXERURhNrbJyKg\nSjEUBwsRJcyYHqQsLMZ5i0DVbXK4NXghnRVcWK6FhZ5B/hrvdc7PtL3My6HZInM8DAOu1tdMDDws\nsUk9ChaLBcgW+3ixWOD6agMmkjPgWjIF6cAM1Av2gGsqLxibo2n17va+416FVEqVgXn4UyZYHA95\nP4SAi4uLWW1RjVXmcoyDidNazTSzeObyfTXPkiAJh577DJNVzezYcRxhTAPr5tzzXddl06QWsFro\n63uU65BrU5rff/LoFNN+l0B41tRHR0d5Duvaf9Hk89bsCuv1Ojfmvby6YnSj69A2PbbjBOcsunaB\n4IUDgXlXgXlXbJkD2dLr5yBDAhfb7Zav+WMOi8pNH8biAeQHLEJYT35tNwIF5qi97RqcD6EQxJat\nyufUs5pNuta4h4m9QHGyBNAXTVULtNjW3vvcalxq/A8DEHzOkhRdO4ryubz9x4i+a1Or8Qmnp6cz\nsFxgt9pskgW/WCzQ9l1udw7FeajCKCj3Zq1H07TQiVtgmnb5/kTYiwKYU40f4sutaVj73hErvVch\nFbC7fuBZcP2UOUFFqwpWWuOk9dZfC5l8lj9Xel9Kd5G6E4mw7NWCWjtM9XnrClR5uBKFMmTydws4\nLtymsYpl1xoVKMV6EqEh4r5I9b2UDtRIyeAmBxgkG6rO56w1qWC1QgJnjIELAftpwjAc4Wq9h1ai\nDAjTlKg3oWcYNVHp4sf3XHanOt81BMoOllLqzm0X77cBrmKoSRtO9BDo6TDsJqA8gNnWVpcmA/Oc\nzTqOz/BQ4YeqH6TWJm/bQKJIDB6m4ToqIQCTLUsbTg7hB8BJMj5MAHnAc/KFDwxBKRA7TD6ANBMl\nhBAAilV/ToBSIrXWGkZw1VRvJEB9diYJKTBRiHVlRxCHTTqqyGvF4eIgwX6zxzAcoaUWdvRYmh77\n/R5+SvYnEQIBSkWAJIdUgRtNFMIL6YRtTAOldA6FNk3L7XM0IWCE+pgL8WoNdVsYTQRNtuNa8GpN\ndhhxOvx9GB2SwQ/5pvCLxqy37vo7QiJzMIa1mXApSVJUrSXzQslcULeXUxARt7tJ2lwTQ1NsZpSk\nFqOF/9TPcg3k+uTvOiFcol/WcxaUNyHvKN6nlt+a26vHGOESrFUHJGQx1M9JnltNkiFIyxQ8FDj/\n96MuaT60AWtcUm5a7KnD8GctdLXgHjonogVuExyZXNkma2+ZqgdfX4+k2fEWyNz9PjhA0v+q65OH\n2zQNIpXt71BI8+dJVZ8pmirGiC7tAvJ+17U3IDtp1ziOhcpd7qmeD17wJptQMVXXBkLO8vcou83h\n3Jd5L92unSscWpE8L1girl79mDVpPWTCDx9iLUzizIg9KUIsx8lrMZaEXNZAOjtKcjxQAHvRtHXI\n9HAB1HaZ9zfT9YCIpqm5n8q9eO/RLXpoHWb9kIqDxvfHpcnzzP8YI548foz9lss9hBkPQGZ3EYdJ\n0IK6muEwqVspzvAC2Dm9vt6g7RhhsYEhwMlaIM7ZS8QJlAACwMGYGh0Rs0IpoGk0Npsduqb9uCNO\ntSY9/JGblpUvq1m22FoDyrlqL18muMZFa4cLKIJY/8i5cKCZxVmoF8k47m5omHrUtrGk/hndZLA7\nZ0KZ1LkvCalSKvFDMY55CK3JuZ8/f57vWRwrudfDojgiQkCJFvkUHOi6DmMyp0IssJ9UltamQ40h\n83coKGXS4i337FNboGHoOSrlP3IhlXHoLNUatZ5w8e7Fe6ztThk1BFRr2sMFwItgbsPmnwNsU66P\nnZZmFgEjAEQRrZlr0vq81tpMyCvnkfsR5yxGzty31mK5GKBSnyij5kkpXO1ZuEYL6O+ynS6/61Bv\njsopA2vLTtO2JYLWti18ctDkHkU4pcGFzJ11O1hnZ/VfTFXmuYlbjEBQiHdMhLp3Lqi6Tr3ekkQI\nRSCz3aY1fAU5RRTNqSvhrKNU3nu0bZfPK14vACgfs5tNQfKWIwLNscraUYhhTL8DjNYgEudrXq5c\nwpYai96AEKETOfBoJ0gryggFpTSi81DGoNEKjWrgww6LruPao+XA3nKQKtHS50oZzc4OAdNeIncM\n70Vo+BDRtj22mx3atsdoR1CKu0cE7GNpiaOUgjYa026X8yRmkbNKaBuzgCKueyIAUihBYYROdq6b\nLPQdU0rvnVUPmMe9BcY4dG5uO6bWuNZaIGnc2j4iIgzDkM/lvZ+FJVWc45dyTGxKhj+AfF11Mgww\nry6obUF5v9i98wK8OllDBNt0BoTivFhbvnOaJihdhXZRNJp8b9M06Ps23ac0CSsNhGsPnIjya2J+\n1KZCHRSQRSrdsOUcEiatAx/yvzYGKgIOFuEmmPFnGh9MSfOhPfcnHQOUKFAd4ZhSZnltX8YYM+GB\n/Ih2kO2+FO2VMpDD6xE71DkHJTBRdd21pgXmVZL8sIu5Ig9dvH9ZQCECWnFsf7/fwxgC94ZifFIp\nLoMyprQM966EeafRIsJlU8IYAzuVZBQQsqNTt2akKgwcY0ytiwrMVjtkJQOqXLssPBnWE3cMBEDa\n3ErS+2cZH0RJs2yPMpm1ppHtXsYhxCTRlqurKywqDVk7DTUrSK0t898SaaoiTsH72YMozoLJlDeH\n11MPObbE5osD0/dc3y6sIDmtUJtSTRpithH3+y36hbC1OHgXEBGzVy82obUWzsvOU3IAOMfUgLKT\nw9ea+aqotPoBkK9L7leQDYG05Nlk29TOaSi1bhCJsV4f/R3JyD8ACEo80lrr1B5pbVuKsMpKrunA\nj46OEKq6pxp2OdSutedfx/uzzRlvySFN49CZqo+RBSbapQ4e1CFXEdyu63JrSe89oIsXrUEg0ims\n2iWqdTDtZfRQymAcbaq3km26RYi21Bp5D0RKUas9mrbUJNX3Uy9myXEVxaGUyu3VZW5lwco91o5t\nnkfvoYwCGQ0/lfqw9xn37t3LViUTUnuptc2VP5d+C+YpE2St5dqfA40mAnHo3cv32/Rdh/H/Qw0p\nDzMEpqsUSOq2XIIaZK+FU96/DfJqmgaaFFzSSm0jLX44Xh8jpwiSAih1om5brnPSmjXpOE7QpuCr\nfK/zBS/XJd9LRIiVMylzUcyQcCNbrEZUDmEpdsYIpHRqpM6a+i7j3rf7w+gSMKcpr4W3FiCZcHE8\n+r7PmhSogfIw+/9QKx4KNTBnO65fk2uThyrJKWKSCMMJcBtBsLohnHINcm/W2oQWpM9Eys5SjAEx\nAoiaCXSrOZmmqTg7YcrCWZspssDr+6l3MVEUtb1af062+tvmKs+L7ECqPLu6bPx9B8U/rcfyMB7G\nPY27l/I9jIfx/3g8COnD+ODHg5A+jA9+PAjpw/jgx4OQPowPfjwI6cP44MeDkD6MD348COnD+ODH\ng5A+jA9+PAjpw/jgx4OQPowPfjwI6cP44MeDkD6MD348COnD+ODHg5A+jA9+PAjpw/jgx4OQPowP\nfjwI6cP44MeDkD6MD348COnD+ODHg5A+jA9+PAjpw/jgx4OQPowPfvxvfOfk0SJNl00AAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-uksCaO2MytI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "9584ac3c-4204-406d-ea01-92765acd9c78"
      },
      "cell_type": "code",
      "source": [
        "!ls -alh \"{BASE}/{df_img.loc[8103]['name']}\""
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw------- 1 root root 44K Dec 21 06:24 '/content/drive/My Drive/fastai-v3/data/petadoption/./train_images/93c9866b0-10.jpg'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "KZDSYcPYP8nZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tfms = get_transforms()\n",
        "bs=512\n",
        "\"Had to set num_worksers=0 to get around an error with kaggle's runtime\"\n",
        "data = ImageDataBunch.from_df(BASE,df_img, ds_tfms=tfms, size=128, bs=bs, num_workers=4).normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_61zgfYTKQ1_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "3a084e7c-eaad-4567-bc57-67e21838ee32"
      },
      "cell_type": "code",
      "source": [
        "!/opt/bin/nvidia-smi\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Mar 20 22:59:00 2019       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 410.79       Driver Version: 410.79       CUDA Version: 10.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   31C    P8    26W / 149W |     11MiB / 11441MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YxXqmuUJKkXh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "a62aa63e-68b0-43c5-ace9-b910f5c0d479"
      },
      "cell_type": "code",
      "source": [
        "#!ps aux | grep python "
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "root          33  0.1  0.4 190780 63372 ?        Sl   21:02   0:11 /usr/bin/python2 /usr/local/bin/jupyter-notebook --ip=\"172.28.0.2\" --port=9000 --FileContentsManager.root_dir=\"/\" --MappingKernelManager.root_dir=\"/content\"\n",
            "root        1993  7.5 17.9 41693752 2391448 ?    Ssl  22:13   2:44 /usr/bin/python3 -m ipykernel_launcher -f /root/.local/share/jupyter/runtime/kernel-3d920225-b3b3-4249-994f-338973204660.json\n",
            "root        2009  0.0  0.1  54376 14716 ?        S    22:13   0:00 /usr/bin/python3 -Wignore:::pip._internal.cli.base_command -c from multiprocessing.semaphore_tracker import main;main(72)\n",
            "root        2266  3.5  0.0      0     0 ?        Z    22:36   0:28 [python3] <defunct>\n",
            "root        2267  3.4  0.0      0     0 ?        Z    22:36   0:27 [python3] <defunct>\n",
            "root        2268  3.5  0.0      0     0 ?        Z    22:36   0:28 [python3] <defunct>\n",
            "root        2269  3.5  0.0      0     0 ?        Z    22:36   0:27 [python3] <defunct>\n",
            "root        2341  0.0  0.0  39196  6408 ?        S    22:49   0:00 /bin/bash -c ps aux | grep python \n",
            "root        2343  0.0  0.0  38572  4940 ?        S    22:49   0:00 grep python\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "T7lrDlvvS0uT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#!kill -9 1993"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "nhXolB24P8nd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "56c43cb2-ac1b-41f0-9ec9-46eb43bd2e8d"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "data.show_batch(rows=3,figsize=(5,5))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GsxSrenR7M6ZpyqUr18hlQM1vgzBYlzGdJdW9WNYosxlRFOL5OUI4Fhpt1rcG6KQgzWbU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\nqykDK6trnLtygeXNNYTvJuy+55xOJ20B4DQMxmUZuIHTSb7mySmgKh0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opZlN52gZUdUV\nxw8fYnsSDDVCUhqH8gLrJUkSY6xFevix1/84j3/2AfL9q1x69BKH7riblRM3c+mRR3CzC8SiYOfK\nDquHc3xpqI0h7UsqSibTOVnWoywrkiglYgIrGTpO6bqan/uRF/P4pz/CS//hP+XBr3zlac/Zrheg\nFkYLgkqQaYZtaaWWnMCFFxdFYTpor5tjmlHBHoXzgt7KKsPhiDPf+jLj7SuMhtvc9ZJ7OXbiJH/x\nH/+S+z/zBVZ7EaB46KsPEyUp6+v7uPHGHltblzl/9mIjmBFuPOtMs1M6pBCkURo6Xoo5s/mMgwcP\nEkUR4/EYkKFIJiRVXQGBgbBgJiy866oq6WcxL3jB86iNaTxhufTEpQztpPUepdMAy1bYZeV9sVkK\nAV7gXMNuMybktBp+rTE11lUYW4Ja5Kc9UTOt1XkHPtygPlI4u8h5hhEmzocNt6oLJpMRHuitrCMc\nQJgFJYTEN5GHtbYpajXq/TLCCYW1YYOTscbJMATOeoGWKnwXAVfOX0JIR117ptYxqgyjEiyKREEa\nKdZXemRZTG335lpGUUZ1TaPLwqEJKaiKbre7ZEc4H/KNSoc88SJ3fW3KKo7jpXawFxIhw+RSpMJW\nFUmqmnw3XN7cpN/vURrPeF4glWYyCayaNJKsrPS4dDlEL3Vdo4Tg33/yY/yzn/99kI53/PIf8KYj\nd/DwA5/j0fGQK1/9Oq997gGUCzTJ1DvKekLPJdgZdGSElwYVO5ROKUqHmhUksQXRYcOM6BUzPvtv\nfw+X7Hvac7ar8eTiRC+M5qLX+lreGjQehTWhlU1HeCGJ4xTdFAPSTo8kSdi8fBlsUGaaTIcY5/n9\n338XnU6XTqfLjTfdzMXLm8goptcfcPLkKQaDPocOHWJ9YyOEgITcqWp65b33S9Fh5xxFUWKNodPp\nLGlTZVEu86cQJmNeDWOvFk6iWDfjNfzyu18VLl4Icvg9G+YLri0fLqaMLsY6L/Kqi86jq+dlwSNe\nYHEufcMVlU1f/kKQwtSmGWHim26qpiOKUACqqpLxeNR0uNWNgRcNi6DRVfAa7yXei0YHlTAhwRpo\nRqc0v8I7S1WVlPM5zhrmZYlKcqzQDCcV1kmU96gohoVIuGxaoPdgMRHA1A7bbASLtIxprnulFGVZ\nLlMkcRQ1/e1myXyRTd47zwMn29T18loXQmFqh/dXz00YruepqpqirijrGqSmNm45UbgoCnBB63bB\n8rHWUpmaKE/RsaAod7j5uc+jcI6nLp9j86mLfPj+zyDSDZwSjEcTrKvwvmY4n+J1AnGEn0/RxQxf\n1YES1TAytHSoPCaKEsT4IvFs82nP2a56posTfm1rn/cgm66RRSgsCN6EikKPt9a6CZ0deEWadTGu\n5u577mLnyBE+pQRae97//vfz0rtfzGgy5amz5/nig19mezgiTjJe99r/gv5KzvbwIqPRmJX+GmVV\n8eUvfbUZ73x1Z11WiJsQcX1jY2lorbWh8u+bNkkhGhEH+Z98T6UFGxtrS/3LRfFpsYnYRiJuL0Iu\nVPKXv7na8hkU8eVSdsD5kIfWjZFsxgYtv3/oflsIlDRObWOAo4V2rAiG2gWdLUA2Awk93jqcK9i8\ndI7V9Q10FIcxGT64xd4apLgqKF5VFVEsQIU0z+Kzy7Iiko3SvzHMphNWU0Xc7VEJwRw4P6yIkfRS\nxUzUJHkXoTUaiLRmVO09OUWA2XyE1pp+GlHUjrmIlwZs8eh9qPYXxZx9+1YYbW0zdVWY4zQvSJKU\nMIbGYIwnzzsoJ5DeIKLQWlpbQ6pjdobzpt20JBYKazxJrtkZjehnVzsYK63ZGc/xMgxPlFiMqShm\nO7zzt36LC2fO8S9+5W38+R+8j88++Hke/NJXiAbrPHxxh5edWufy5mV6K0dZS9fpHD3K1pnzVGVB\nnPZwxjObbpPEOc5NyDqrlGVBz9bMV1NOySNsV08faeyqMQ26nR6dd0NBx33nuAm8QKBQimvmjy9G\n/wqMc2SdDk5CkuasDg6idETvW9/khhMn+YGdmq0rU7Znm8g0o96eIyT8yKteyU23naLf72PMTVw8\nd4bL6xfZ3t6krudMJlM2NzfZ3tpZGno8FOVkWaBaTGEUQBxphBQNL1YHj1mqRqXILjukViLNvXe9\nEBXFJHGMqSqiLENASCc0A+eiONq9RfkuYYxZEui5RkDEufCzIOS6ly4f1zZmiKWilGlkCnFgvUVH\nqslbLzqevjO/Ll0Qdza1adT3Pc4Hg1nbsCmvrK7hoxi5eJ71SGWxJlxrUopmbLTEO9vQ4Zp8rYco\nTkB4Bt2czYvncU5SO8PF0ZzN2YxBJIh0QjfvorSmmyesr/WxPlDi9iLSNG02rRrvJVL6pXe6aKNe\nOBtxnLCzM6GY10RpcIB6/R5VWZPnnVC0k24Z3YXXBIdICgU2dLsJ4en1ekzGYyQFpoiJpV6udZ7n\nCCGYTCZ4Z8nTmPlsTpKFaRZ/8tEHqOua+Dd+iz/+dx8k73dZOXCQ2jj+zce/wM37X86BfIWt6RyT\neuonnsB4S7fTZY7ARYKVZAOvJKsiYjIZEzlPVRlULMhjDcnTm8zdbScVCwEBhYwErgoLtSDpB1Wm\nqx6eavKowRsMHTVlWYUpojJBKE+/v8YP3P2DPPXEtxkVhkOHjnC0e5gPf/gTbO1sc88993DkyBEG\ngwFaR/R6CWuDdU6Wc4pyxqHDj3D+3BmstQy3d5hOZzz55LcZDoeUVejZXbSYwqKgYkO7aJJQFGXg\nqyq3/I6LcOjkTTdx6uSNS+6l1k1DglLhwmqKNlW19+YGLUbiimVl/KqHvdiMvPA44Zq8qQVjcSLk\nLSVNHtR5fOWads2wIUklEI4mBJeh7bMZ3ayaQod1Fm/8svPGO49zc4piRl1VJGlG3u0jvCNptGWL\nMqyV0s111IjYIMBVBZIIqTzeGqjmCOFYWVvlyqUrnNmccmFrxrwSZH3NtChYTbv0+x3WVvokkefJ\nc5tUUX+XVuSZYTIOwi3GG2ZFjZDRsg6wiA4XUVuIwhS9tT7SB1GhNImZGIM3FbWpQqMFMrwHgRpl\njGE4m2CcwVlHnCQNayZCaUFdF6goRhB4rufOnyfL4pDOEYZ5URNnCUJGSJmg1JQ873D/l7/AviOH\ngr6x9whrqWrDn37+YX7+pXcw257gkoSol6HyDmUxI1vfQAvFZDJBW0OdJqyoCGFK4kgyK0tq6bHl\n07d677Jn2uRG6xqJJ7mmwygoRBmiKIQKi3lNi9BMK01ZVSE8dhEWT1HMOX36NJcunmM+GnLowDqX\ntq4Q7ThipXjlK1/JPffcw+rqapP7CZzTOOsh44Ss1+cWIeh2MubzOYcPV4zHI6azEdbVGBPGqdR1\nvQxzFh7zIveXpinesxwi1u12loo6L33pS78jP2qtgEaMevHdgne3a0vyXcNaByIYQRFSlCy/iF8I\n8DpwNoxntvaqEFTTjGFs+D8pg7qwaAj2YTRMs9kQWooDq2JRJAyC0l408n5cdZBtXTEbb3P5/BkM\nCcePH8c242S0kkBF5EMksCCfh1ZXj/SCWGmMs2Brkixndf9hvn15xLntbYxISUVJYRzdfka31yeJ\nYzppRGE8yIxetjer+U44HCnC56yuKsaT2TLdFUVR410G1km4ZyM8oSCktWIym1FZSydNGcQx21vD\n5XPRIGJNVRUoB856hBbUZk4YJRNT13M6WcRwPKM3GGDrghPHDnPu4kU6nQ7DK5vL5oGyLClcjXOK\nul4IJRnSNAiEZ1nG3Bg+/60LvP75pziYwFZZEsWanp4hVETuwcznaOGoiwk6SYmzGFMpdK2JfJig\nvKSt/C3Y1UqHtYFeVDsLUYQhQkYddJQgVYTzQdhiYUgBhIrwOsUiUCoKeVYLJXMubT6FnUw4/9QZ\nhjtDrmxt4pzjybMXWT+4nzvvvIPV1UEzXiTkZ+M4JYohSTR4RyfvMhisk+gILTSdtAelQdeGjW4e\n5ocHiSDShj/nbHifLOsgRPCoFxfdvKxIkxiFJe/H+Fgtq6CLv4ucaujGqXF+73mmXngcFiF8oEf5\nMCZvkSZZ5savec2CwbBY32sH7l3Nt9KoRYmmLz94jgtPfhECAo1MYvh8gV/qnPrGexqPR+zsDBmP\nx8iGCWBM6LwL7AGLt26ho7I8NoQi6/bQeQ+nYi5tjymdx9iwfolWZFmCzkBnKfgwx8g01KC9iN5q\njvUVoJnN5ggBcRwRRXqZ5lqE7N4HKb35fE5RFCEMR2KtYjqtqWvTrFdg7AgZNs4oSfDekaYJELSM\nrTWkaYb3jl6nw0qvi/SOWEtMVdLv9xqaYSMs1HQtuqbhQ+sImsYPY4K3G0URKIf0gkcvFoAkEopZ\n4agrS21DTtzWBlMHAZxYpUgRYbyn2+nQzTLABUGdp8GueqZRFIbaRVkG3ocT4UMuNZCn3ZJisRiC\n5prxyM7aZlgJODPniw/8FefPneXRRx5me7RDVdVhhr2KuPWWW7n33nuDipOOyLIMjyBZnGgp0Uo3\nog4dpGxmPckZMGM+nxNFQUwhqSzT+Zyd8ThUmRHoSC0nq0Y68OMWu7ZQweO8VmlKShm4lU2ld0n/\nkqGIcq32515BbWqkWxi6qMk3NhqwTYtlU9JnwZQKCP+wzc3pCaIi3rkQsSiJbgytMYZIh82oLoNs\nntZBs8GYqgnv3ZJq5hsagW+Me6+Tc/bJJ4mTiEOHDtNfCame8NnBAEod8q4hYslASOJOH2yJSwbU\n9ZS5UWBL8CUi0uRJxNrqKp1OjmqKMuOixMuIqiz+rpfiuqCcCawRzOsCjyDPohA1AZNJcbU4LBZM\nF4tnERUEqloUWVxdY+oMKWOsrRHSUlU1ahbSZTpN8XVBnqXUZk4c5UzGm6ysZCBdGGhYFnQ7PXZ2\npqAEKysrIH3gfs9LIq2oqhm1kThbIXSEd81GiMA6Q6fXYzab8cHPPcIP3vrDJG7EeDKk11mnVzmG\nly6QpCnNtrg7AAAgAElEQVQqyzCVwHpLnER0VZfxcIuyKsizDsinbyfdVWN6/vwFkqxHr7mJbKPA\nrhvzr6Qk0lFTyauWO2JtCc8Rgvlsxr//8w9w4fSTOA+jogAVs75/jTuefQdHjx2n1+2RpindZldT\nShEn+VUJtzheFiCsdXT6A9b37Wdna5M0TXnJvffy2Qf+KvRax5K6CSOqqgqULXN1d+71eksBaedc\n01YYcejQoaUnVRF4d4E/KxriucWVjkSm13SK7x0sDJipg1GUshEpWXrdYetbcIoXj0sPlGYGlvfU\n1uKcJY40SRShpCCSsqkMO4ytiOPgmbqGOlXXVUgfNCmhhfH1PuRPp9Mx1ik6eUpZFFy6cI55UbI6\nCN1reEUc6aCZIATCNtJ8UpGmGUpkRFJQbs6Zbm8TSYWOFEkWceLEDfS7KVms8NZRlwXzosYLuUfV\naWFczJaRQqfToZOEPPOs0aRYCKsv7qE0TUlSjalDpDAcT/FeU83m6GhOnudUpQneupBMJgV5njMv\nx/SzhKKY0+nk1BVYI9i3cZCty1vMpw6jJIVxWDTKw5XLQyItGY/HSxaM1hoRhWjVekmn02EyHpLn\nPbwT7Iy2g4bx3PHXDz/Fc46v0V/fx2Regqrpr69hpcDUVaDIuQrve+g4IslqsqxDOZ1Rj75H9UxH\nO9vsbF5mY20dH6dUmOBNumbMr4C6kaaTSjGdBZWmREuMdYxqw+WLZ3nym99kuDMCpZEIOr0uP/zq\n13Lg0GGypkdaqdDLr7UOlcqGaiOlRFiLFpLaepROsMUIbw2dbsa8gGOnbmRaV3zq458g6+RIJeh2\nu81iKoSwTTHNMh7vXO0Hdx7rDTrqk0cdjCtD7o+rwiDWOfALUnljlOTe6+euTb3kiwonQtqEa0N8\nh/My5EDdomUYsAYpg3xebSqMCyObYx3SLtbYJn8XFPejSKMjjXUOv5jS0NDnGvMcfFHvFyPEKMsS\nYxzbWzukcUamPGa6w9ZswnjrEusHjrC6vi9MP5XNlFvAO4MkeEe1d0SR4rOf+wJlacm6GXEScfSG\ng6wNBkjhiCKJUCZMvESRJWrJPd5rUMKHe63bIdURXoEpHSrJKGbz0HWWxQgBs2lFXYOUmtpUQd2t\nSVvpTraswIcCsqXb7YYW1aoCJ0jTGO00ZVUym0+obcmlSxMqA0mvB2WJqTyrgwHT6RTnDWVt6HU7\neGsYT0N6QQiPEIr+yjpaa3q9FaaTAhCYek63M8AoxV988xw+U7xoILE6JesmSOdxtcHgl6yB8c6Q\nSMZMrlyhf2gDneek/5mGml11gcqyYDqdNnPqzdKbCHN/ApQK2pXGmmVosegX9h6G20Occ2xubXL2\nzBnOnjlLr9fj+LHj9PsrpEnyHZ1Wi+T54ueFhJ9r+ralkFjnKKuKsgqdHGmacuTIYV7xqh+m3+sR\niauK7c65RqtULN+3KAqqslqSzov5nG9/+9tIGcIPfHOTNfe7Uuqq4ElzDvYarLUYW4cUjRDQFG0E\nDTkfgfM0EwWCp7oweuG8BW/SNdNKrWlGX8tGZu9aPrINz1/00dtmLVTzfGddIxwumsYOxcFDhxis\nrzIrJkzLKaW3lMWU+WTE+bNPsjPcwtkaZw3OVIHrbC1VWWKqCuXg6199mCvbQ07cfjMb+zc4cHA/\na/1VtkfbTMbbmLoMFC+CqM1CS2IvIo4yBBFl4RDCNwP2gpdalqFJpSwsphZIGS8r/b3ugDjKEUIx\nGAyutog33PDV1dXlebHWYr1la2vG9mSHeeWJ03Wy7j62xsPlRrxoPd7c3EQpRZ7naKVI0pQ4ijl+\n6DCr3R43HD7I0UP7UV4QS4WWMXEiUZHh2LFjxHEcZC5VzCe/epZtE+FVyvaoYlzMqUUosBVFQbEz\nRHuLwzFYXyOJIzQO1NM7Oru60nVVM51OGQ63WdvYWNKkgrcWbjhrDVpopJDLk7tIfMtGWHhjY4PJ\nbI5xnq3hDi984QvRkUYpSRqHIlWapvjFWOHG4EVRtAwJww3tsdjl55RFsWyHnEymHDl2jCxOOffU\nGYpGoGOh2B5FEfP5fKksHsRzr3pmo50RWkV467DWI2Xw0HQzrkM2xlgpz2K88V6CswaBwjVapc67\nMILbexALLdNrqvs+6Ij65ufg2Qc1/SCO3cwC8yHtI8VCOewqP3Uhr7eYcODE1WvD45cpFCEgT2O6\nnYzRUOGoUVFCpDRSR5Sm5tLFC+zfWEN416QkHM4ZnJV4q6hLw2PffAJjLevrA6r5DC0lVTmDehaG\nJFYVxDG1NYBeqoDtRVjrsAJ0FIHQCB9kMifjHbp5jvEC20hnaq2IdPiedV2TZSmzYobFI5QKs5q0\npq5LvDdUpiKKEiIpkEYSxwmjnQrlPJ00oaoKIqmIkjiMYl/ksJs6hDWQxil5nFDUNZuXLlNXc5zv\nMJlM6HZXGE+2UTqiKGbkWU4SdcL12Q+ty4UxjKWka+cUZUlRRGidUFeeTqoROsKYOsz+Iqaa1SF/\nHj+9Pu3ujnouK+q6Iu9kbG5fYWWwgnAC75NmR7cQhR1M+MBldB5qa1FaUVUFx06c5PL583gZ44Xg\nR2+7neMnT9Lr9ZsZQhKtgyCx1BG+UW3PsmzZ/SIiTRpFCKkpqxqtJZPJlE6e45xjZzrm6JGjpEnK\nwbX93HLTLdx+55184fNf4Etf+hI10Ot2wQcVft8ItPQaRX1rLNOqoKwrYiVAqGVBpjY1Sit0Q7vw\n1uHYe2NLXHNerbFUBJ0Ch2tU9Zu8llIsmk6DXF/TrivCjKWFqI11geAvRchjCu9Q0qNUqNxKwNYG\nJz2y8VpFcwyLnv2FoLS1hlhBIj2rnZjshsMImskAtaGqDeVszHxeMRvtsLI6CBxYpQlzN2Dryhbn\nzp5jNB4Rx4JyNkFQY2pLJCMSJYjTDCtjyjo0IZiqDq3De3BjBBgMeszm8zAeZD4HfCOH6YhSSe0l\nRgVeaL/fAyyzWUWSxGitWB302d4eBo4uCbPZDCEsnc4KSkWUZYVCEMVpmFLhQDjPzjAIR3tjmc3n\n6ChCidDtNB6PMSbQ4PI8AwGb25vEsWZWjVn1G+R5hzyNGayucObcGeIoIolTJtMpxhZorVAywkea\nP/7Lh/lvX/N8VOwYTmaUpiTPe0xmc/KVHro01NsT9KBPlMSY0iLl9yhpH0Jr4eaVy6ysreGMIYoj\nVvprS3Fa21CjlGsKFC5U9JM0akQuerzk5a9gMhpRG8NgMKBqPMPABaUJK4K4xULybyGNF3QpFVVd\nE0ehOi+VpChKtBTESczRI0eDN+Q8xhqSLOOOgwc5ceJG7rr7bj7ysY9z/Ngx/u0H3of3DuMFVVly\nuSg4fPgw3ntqZ5nMpqz3Okilmurx1Sq3c64ZXLZoj9xbCDlih2+6wmyjoB8eHVLJJrfpQ77T0xQO\nJBJBXZfgHaLxUpES27SdRloiRIgqFor6vpkmuizeOXA46tqgdFhH61wwstYgsXQTTeId89o0A91s\nuKlljDGWJ05/i2f1nouOYqSOQpTkHU+ePcPW5iZeegarA2xZBM4hCukjojTBSY0jojKm6fQKDQgy\n2nvMDIDxdEQnTsmzFGMdWztDrLH0ul0SWTGtDELEIULUkrIo6XS7aCUpyzlKSo4eOkBZVQyHc1ZW\nBtR1UKBKo4zZaMrq2irT+QytFdaqhrLosM4jIx36/2Uo6i0Kvnmec9NNN/HUmScYjidsHDxEJ1sh\nii/T6YTpqMJ4yvmckydOcvqJb1NWFcV8Rp7H5HkXrSOuXLmMixRfOrfNi2/Yx2hniHVBDyCRGjcc\nk/V6pHmOrQpENcVJDcn36NiSuGkb3bqyiUQiV1bQiGW4rLVGi9D1JJChMCAc1kJZBMqJUgpvFb3B\n6lIBKNX6O0bMLvIz1woFm0a1CWiYAgmz2RSlI86ePUOeZWxsrKObRU2ShNpAt2k/rKqa9f2HWFnb\nx2Cwzv/5e79LpmOMD7k6pTXRNY0GxhiG20NWuxneWsBRGxOKKU36wSuFVtGeFDpZhNYLSTxhDK4h\n4yNCOyLO42TTVopDSdAykLZlQ74XUgYRE3eVVuVZjHumyXWHx6IqGk5o46Eu+vmbz/HO4BGUhK4y\n1xRHvAu58vFkgtIa4y3GWoY7Owx3xgxW15ENrWo6m7G+bwOpFIObT7Jz6Typ1jhvm3VsWlhdRawj\nVARCaOrKN9HT3uMMA5S1o5srKlNR127JYHHWMZuXqDgijQVadzC+xlpHUc5wJmx4znpKX7K5uclg\nZT9CQKlCwdcZw+pah/F4k4MHDnFlOKQo5tR12dyjLmjPIlEiXCRLtogxPP744wjhiOKEbq/HhXNn\n8C5Dqg4CSWUdMqoxdRUcJeM4eOgg9bwk0gnWWfZtHGB7uM0nvn6akwf6dNIMWQ8pZiVeGzqZQDrD\nfF7R6+TUhUPF+nuXZ/rK176Ov/rLTzFYSalMTVlXTOczVhHUVcnKyspSfb1uVGcWBtAJUEIym04b\ngYQqKD3JIPWWZfnSMw2GOUJrtcxpRjpvBtgJtrc3KctNOnmX0XjIhfPnuOXmm6lNRW0dcRKOr5N3\nSJKw+5WzMSIZ8I0vfop3/G//O1VZNpXs4G0hJUJHTGYVa70e+9Y7xCrBewWiJNJpM4DMNsLQC5Ul\ni9B7z5tZDA70Phgzu2C+u8A1FEISvtbS5IVCklZBu7QZd22NCR4uwKIgqRUmCI426xnOWZ6E7qJy\nPm+0b81V2UMEWims89S1CbzjkEmlqkvmRUWUxM2wPhFuWCST6Yw074JUSBkKW3mWw5qgnE/wHspi\nBtIHj4qKorQIZ0gj1WzgICNBaaplJLTXkOkcTM2kLJFSs9LpYuqaRGno9CmtwZsCSUZZega9DsPh\nkPEkGMT5bMLqoEev16WuC7I8pZgUTTooIo4TuoMNJtMh02mJ83UYs+0q0ihiXiqkdssC1KJotdAM\nsLaiKErKcRmUweKM8XyMNTXFdES306cqpzhCveSpc5dY6faYb4+pqpo4jljpd9i5UPGn93+R//Ll\nz8NISTmrcVkFAup5SXeQYgtBLNNA6rdPn4ITfpHNb9GiRYsW3zX2XjzZokWLFt+DaI1pixYtWlwH\ntMa0RYsWLa4DWmPaokWLFtcBrTFt0aJFi+uA1pi2aNGixXVAa0xbtGjR4jqgNaYtWrRocR3QGtMW\nLVq0uA5ojWmLFi1aXAe0xrRFixYtrgNaY9qiRYsW1wGtMW3RokWL64DWmLZo0aLFdUBrTFu0aNHi\nOqA1pi1atGhxHdAa0xYtWrS4DmiNaYsWLVpcB7TGtEWLFi2uA1pj2qJFixbXAa0xbdGiRYvrgNaY\ntmjRosV1QGtMW7Ro0eI6oDWmLVq0aHEd0BrTFi1atLgOaI1pixYtWlwHtMa0RYsWLa4DWmPaokWL\nFtcBrTFt0aJFi+uA1pi2aNGixXVAa0wbfPKTn+TWW2/lzJkzu30oLZ4BPvaxj/HjP/7jvO51r+O+\n++7j0Ucf3e1DavEMsVfuzdaYAvP5nLe//e0MBoPdPpQWzwAXL17kLW95C29/+9v50Ic+xOtf/3re\n+ta37vZhtXgG2Ev3ZmtMgXe84x382I/9GJ1OZ7cPpcUzgNaat7/97dx0000AvOAFL+Cxxx7b5aNq\n8Uywl+7N73tj+o1vfIMHHniAn/mZn9ntQ2nxDLG+vs7LXvay5c/3338/z33uc3fxiFo8E+y1e1Pv\n9gHsJrz3vO1tb+PXfu3XiKJotw+nxXXEZz7zGd797nfz7ne/e7cPpcV3gb14b35fe6bvf//7uemm\nm3jhC1+424fS4jriox/9KG95y1v47d/+7WXI32JvYS/em8J773f7IHYLP/uzP8tDDz2ElGFP2dra\nYmVlhd/4jd/g7rvv3uWja/Hd4IEHHuBXf/VXeec738mpU6d2+3BafJfYi/fm97Ux/X/jla98Je95\nz3s4evTobh9Ki+8C8/mc1772tfzmb/4md9xxx24fTovriL1wb35f50xb/P8LH/vYx9ja2uLNb37z\nd/z+ve99LxsbG7t0VC2+X9B6pi1atGhxHfB9XYBq0aJFi+uF1pi2aNGixXVAa0xbtGjR4jqgNaYt\nWrRocR2wq9X8T//ZH6GkQmmNUgqpFB5IkgSlJEoKpFRIJREydEHYXNCV65xfUaydu0LcyZBCgBAo\nKXHOIaQET3idkIBHKQ0yCj+L5gA8IMB5kL7ACYE0Gpn22Bp0mVw4T4Rno3ZUkUQjsVUNUoTPEQKL\nw1mPQFJhceQ8cflJbu6tor3ACShdCYAQAqTEe8+i7ue9B+/RSuG9xzmHNZ6bX/j8v/P1eCb48z/8\ndXwxY1A/yWj9hcTDC1SrtzI1grVeTkLN5tYYpTWJeZSejpk7hyRDK8n+jXW2Ll9g/5HDnH7ySXQU\nk2VdYgk78012diaoes6BjYNMSJldfpw8lvTW9jG1ETrK2Dz3OMqMOH7HjzAcjXjqW5/CWMX64CY+\n+eBD/MEHPsyk7pK4CuljLBYvwAuHdw4JKO/BeUphsTphzY35hTfcReZLDhw9jk9n/Pn//ShdMae/\nITh+637qKbh6Gxn38arDl79xjpvveAWv/Hs/jcw3eOmL915L68WL54FwfVprUUoSbhzPhSvbvPmX\n3grWASCEQiCweKSUCCnwziOkQCBwQpBKj6kt0/mQTmeds2eeZNBfYV4WJEnK6uoKzkgQjqqqAMXF\nS0/inGR93zqbm5sU4210kpIlKTs7Qzq9AUor6mrC3/+Jf8BP/5N/yqDb5+1v/1/5hV/8RYRSXL50\niT9693v45//iF/nAv3kfP/3T/4R//a/fxRve8EaUkjz88MP8yZ98gFOnTnHffW9CSsn999/Pvffe\ny87ODh/60Ie47777GG2PyLsJUnpuu+22v/Wc7aoxlY3x88aglAKaxTMGvEIlEdYavFcgQesIWVvK\naotc9blcVhyMNVYIlFI4axFCIBGIxuBJGSyncxYhI5w1jeFVy89XeLxPsTpltp6zdekiRx/foZdL\n8BKvFbH31Ca8dmH0oiiiqiq8CxdZ5GsqVSKPHGQ2rYidR2IQzWuU1rjmtcDy9wiBlBJrLUJIomjv\nBQzZzgSBYBSndMcThhvPwUb7ODSIsPMJFzcvka+eBF8j8Rw6cTPfePzr9AbHWRusUMx2GG07Jlcc\nN9x6N4888jXy/ScYG0++fguT5AqH1zqAwBcxg/VnIbwl7q9w+rHTnDpxC8quUo7OUyeH2Z7NUNEq\nnor7P/cg7/7jTzGrHFFUoE2EcxIhK6TwlGmCdxJXVjgZjKt2Fucytm3E/3X/t3j9Dz4Pe/oCR08M\n+JHX3MnH/+Ij3Hr8Vqys8UKB6OHLBIfjWUeO8sRDn+LfjTa57+f+5W4vzXcF58L1uXBGpIyWTkA3\nT/HGgNTBoWgcAiEEHo+3HgQIguMgpKAqZ0ilEc6wdeUC4/E28+kOZVUTJwnnz3+bNO0Sx5o4TpAi\nIs9TJqOSYlIQy5jO2gEm85LTp79Fv9cny1KqqmQ+LbCV5fxTZ3l09DCRlrzvD/+Aixcu8sEP/hmH\nDh/i0z/5Kba3tnnvH7ybfRtrPPaNhzh8+AgqSti/bwMpBV968Iv0+n3Kco51jg984I/5oR96BUII\nHvzSg7zs5S/Be/e052xXjWldG8AjvaIsS6I4QkiBdeGCpvbEcYyxBuEdQnikkHgcyaTggpSo2jPO\noevCgiIlzlkkKhhUa1C6uRCqEiljEBLra5zzwfNNB1xUinK6xeDshCNaUncipDcgwDsbDlgsHoLx\nM8aghMILh1SSycSQRmP2d44yq86TxBLpNUJ7nLU4G95HAgiB8wKlIqy1OKEwhIWSSvwn5+p7HWXx\nDVAazWHKrGBt33Hm1ZQHP/dp1vOYZLBCaSd479HZEeLVG3j2c49w6fKQOB+g03Vu2XeSuhoTYXne\ni17O6fPbrB48ghQCtSOQSRcnFZks0TrBmJrCKQ4ePcW0cKwevgV38EYKkXPspufyjSunUX6bO28/\nQe0/jnYprooYJTWxNLymv8bN+/Zx7/pxvuKG3HXsFo7MFLq0DIXnD888zHu/9iBPbvd4759/nv/u\n9S9g57GzDO46zt0vezYq9iHqULrZCEu8swjvObyeUs83+Z3/8c38zgc/t9vL8/8ZwRGRy38vDKn3\nnjiOUEphPdgmQkPwnR6pEHjvCL4peJUg8AiZs76/w7ysSOOE8XjCiRMnMb5Eq4SqLJFCYIxlOBrh\nqTBmAniGO2Mm85JIR9TGcPbcWbSSlLMpX/jC51nf2CBPY+655x66/T433HADH/7If+D3fu+dpGnG\n5pURb3jDT/Df/0+/zrOe/WziKOJ9f/gefvZn/xlPPfUUWmuqquJXfvmX+N3ffSePPvp1TtxwjM99\n9gF+77d/l5f84Ms4f+Ecn/6rv/xbz9muGlNjDZFuRAyEwDmLdx7weCQ0XqpUwYvUWlGZmlwoXDnn\n8NqA0aUtYqepdUgXuLoKIT3Q2KZrvEJwdo4UAqkSXNrhkvPY8RZrdsrw3IQHTz/Fa1/+IpQrsF4i\nhFh6kgixNIgL73Tx6LwnyRJUVZHvDNnqJESjgii2ZCp4pFopTF2jogi8xxPe2xiDFhLtm4vQ7j3q\nb9RZB7NJlKZ0jv4AT55/lDxNue3UcbSMsTLh0bNnuXL5MrfefBPzcka3k3Po8D5CxiMO4WS0jvcO\n5xzHb1hByHCj3n77zRhjkFIgpcO55toQEuFDJOKFBwmudETUAFghqH2Jp6LKEnTtWS0nvOvv/TTr\n1RS3tcm3H/s81ahAOU3ZzxkrS9TL+anpBq9/1Rv5n//qP/CEUfzZAw/x959/gPqJISbdwVjwTmGY\n4IRGOB82WiXRkQNXcPtNe7Ol1XsbHBsPDg9S4K3Fe0dd12xvb9EbrCOFwC9fEwxpuLZZGlnnHFpJ\nhIO808ED1lhIBEmcopTGO0NZVOhmY1IqYjKakcQ5cbKCUhJjI5LcMhwOiaIIHSfUxYxeb8BrXv2j\n/OjrXs+DX/wb4rjDJz7yH8nzDiv9Pm9961u5cOECOztDdkZb/A9v+2X27dvH0aPHePDLX+a2227l\nxIkTCCFYWRlw4MBBnvWs27nvTW/iWc9+Nkop/vh97+OXf+WX/rOSjrvrmVZVCMulxFaWLEuRMhhV\nAGcdqsmjCiEoyxKlNJUON4qcjnhkssULksNUziBsMEYA1hiElCEP2+yoqqzwnR5WR3zzyiWSrW1u\n3NjAWcknv/RNfv9PP0xv/Ri9XsZdz7sN4UXwGp1DRxHWmOX7AsvQRjbhujcWozWeGWvrx7g4OcdJ\nHTaFKIqCIdUavA/fy7FMFwi4+hmNwd5LqLJ19LCgUDlHbridnfoJTp48ztbwMusb+5lMDB954PMh\nTBKaLO82EYJZ5uSEgqqAKEoQwjGZjInjiPFkjHMWayzzYsZsanHOsr29idaa8WSEVhqtNSA4cHA/\nZjZnXgmUmCON5sSBVb59peJADb/8Y/8IffpbXBqPefLCWV75xp/iwp+8n3U1IBobIu8QZYVXhvV6\nyv/y8tcxTTr83J++k/fNx/zkC24jPdWncAWlqrBWhYhJ1CAE1nqUcsCcna2Hd3tpviuE9Fi4F93y\nOgeLJ9KK5z//+Tx2+olw3Td1bBFieyQCicRxNZXlnMVUjiSNsN5T1xVK9kLk593Sk1UqxlmH947p\ndIKUMd475vMS7xxpHoP3mLpGqAghBKtrA/Yf2CBNY7752KO8+O67+Noj+3jd617HpJjxpjf9FB4P\nPqQSna/xzvG1rz3CaDrjyNFjbA+H1HXNJz7xCV79mtfwtn/1r7hw7ixlUTIZT5nMxvw3v/DPGW7t\n8JP/6A1/6znbVWPqnKOuaqI4QuuwI+FAa0VdV0RRjGgKNlVVoZRCSY3xDpRAVQW9Iwdxc994eEV4\nDaG2pBpDKoXEO0cZS1wcQZojpob9B1ap7Bzr4G+++jiJ6nL8ljv56Kf/mhc993Z0ky/S1+RzpVI4\n75FC4K4pJDnvkVLgZSiGaecptCDyCidcc0QgF+9FuNCUVAgBrg6G2nmP3iOSY9+B+jSGPkYknD77\nBONhjSdhVljWVCj23Hzz7URaYa3g9BNnMFWF0hohBUVRAJ6q8GxtblIUBVtb2xw9coQo0mRZhpSS\nra1NirkjjmOsVXhP0yrqSJKUJ06fZjrdQTpHLD3SWwSK48ePcWn7MW7tJ9ww2ubCzhb7u6vcdc8P\nMawqktU+haiY11N0FtN3MbX0yEwjqZDDCT9w6iY+/uRjFDpGXJrQ299nas7jUCE/KB3CC5AObxTS\nRUC1ywvz3SHkSkPYjvfBoIb/wXs4dOgwjz3+xNXnE/KlwofXLmoBvjHEuGA0nbUhtSZC1FfXZXMP\nSbSWTZQXIhNr3TKdZq1BSoVSiiRJiOIEpKKyFd1OTr/fxznL5csXsaZmOp3gnCNOErz3RJGmrsP7\nWePxHi5cvMD6+jp5JydJEqy1ZFnOG9/4RsqyxLqaoqj5xMf/ku3tKxw/fgPvefe7nvac7W6Y7xxC\neWpb4YXDeYVSEmsNcRzjvcfUFSCIkiwYTOegairpkWZDdbhw6Sn663kwdM5SCE+KQlY1QoFJEqp0\nnUubl+hNL9CLI27Y10FiEV4hhOcrX/kaL/iRn+CFr3gJj35lg/FwxqCfB4aAB9N40QiBF6CkwtYG\n62ww8kqFnK3zSCtYvTLl1NEjjM9eIPMOIT1xrLHNxem9R8mI2obi28JAS6Vw9umT3N+ryLFMsj6H\nbryTP/yzj/OcO57DRz/xUfrdnIcfeZSTt9xBf30dV5VMti6zeWWbOE65dPkCznmyLKOqSoSwxGlM\np7fGyZtu/H+oe+8oT6/yzvNz733TL1aurq7u6uqkVrdyRDDYhMEGY4sxM7YxweBhMF57veswg3d3\ndo/B3mW9fziM7RnPGdg1A+MF28cekhkYCUQQSAKhRlILSd1Sx+quHH71y2+4Yf+4b1VrzyKfs/6n\n3YHUehgAACAASURBVPccpT6ng97wvM/zTQ/VpIKSkkqlipSSyckJRsYq/oUtSRIpFUVRIKVifv4A\ngSjIcs3DX32BMecY1YL/7r7XszR2nCMkLH/1S8j9h5i/+07iKOT0k09ydPYAOk8R1mP4hQYdSawd\nUM800dDy7LnnaakJPvP4U7zt+HG2NzcIjwhSmVIY7ScUIcqiIHA2KmGr6+9oB4EQaKOxxmKMRqkA\ngYVCk2WpL55C+A/ILswvXqJUKX+obDS63R6ra4vU681SsaPQ2hO0uvCQmTa6JIZBSP8LpGlKEEiE\nVBjteY4gDJAqJBAVDhw+zM03nyDPc1ZXV7GuAATdXpfpqWmsf1B2G6y88HBEa2uL++67jxfOvMCh\nw4dQSvG1rz3E/fffz9bWFrP791KtCr773cf54Ad/iyBQ/O3nP/ey1+yaFlNTsu+h8FiijCW68Oye\nH+kVYRghpfDAtFIIrf19E77/rK9t8/RMwB2DnFglfmS0FgW0mhUKFXJldY35sMf+pArSiziMEZgS\nRiiswlQmqE9MMz4zwk3Rvfzxv/t9/udf+llCZygIdp8May0WR65NKeeSFFqXRbd8eoTA6T7VjuBy\nINgrFCEOjNlpUHeL5s7474m2kqCS1x8B1Wu3sc1Jao0j/ML799HbHjDo9RDOMHf4KJ1BwdT4GNmg\nx81H5lBBwMrKKnsmR+j1+oDGoOl1exRhyGaasnDxAsdvupUnnzxJs14jDMLdsVMp6V9o59BaY61l\nOBwyPT3N2dPPkQtFYNeZqcKBNcMMljsn58iEZeqnfhKhmihhWDx/lkYSMDk7z+WFy6jC0AxDTGBo\n5o4oVQx6PZ47v8Ce+TlWL3Q41XYc2W5xdGQUcTmlvw8q0hG4BInA2hwCgxMFkuvvXkIpgrLWP/ai\nxD9LmRRAp9v1ZJO62pF6UtWCkJ71EFf/352RZGmXerXK/PxBLl26RLVWIwxDnLNIqRj0e1QrdY+N\nC0mS+AYqCAKPvWqDVe4lkkdfHJsjowgBeaEZGxvDYTh4cJ6nnnqKubk5nHU8+/1nGQ4HTE5OcXnh\nEouLi/S6XcIg4OTJJzh65CjWOrY2W0gpOX36DLP7ZhBITp16ilq9RpEX3HbbbS97za5pMc3zDOcs\nzsldHFIpRZ5lCOnHgB1plBM7hI9FCulB/hwGasjB2iS2u0o6HBKEEUZGnKfAvNhlql7llmqETGoY\nHMJIbOEQgfFAuDFI5wgCSdrr0LrcpzrleOM7f5FW2mY8MihhcKV0CSF8Qdz5iiqFwMu8KB8+JQRb\nFcN4f8D++TnM4mUMltBIXADsqAGMBmfLbtyUUpTr8+VrjNQo6jeiEsfmxgbTk7Nsrq8yf2A/m50B\n/eEAJyyRlHzja1/jK1/9GuvrG7Ra2zQadbqdLmEUMjo6hpSCmZkZRkdHmZiepd8fsrq8jHDQHGmy\ntr7GYDBgMBjQbrfJ04LhcMDGxgbv+fl3839/4i+YP3iEm47vYXJsjFoWUagC7RyikhCoJspasv6A\nuFpHhCFBNeFbT57kjmPHSfp9pkaruFCxkPX54qlHkIVgj55gGGQ4GfCl0z1ONFPefNsos2aSzWyT\nLEn9dCHxcjkRYMz1OeYjBFrrsqA6TxgFJdkkoLW1VfYzDhw4xG7x9IVUXoXYcOAsw3TI4cNHKApN\nURS0Ox0cXr2DswincM5gnQFrsFpA5N+tLM2oVuo4Z4ijeBf+wzlGRkawxlDkOfv27cc5OHjwIJ/4\nxCe49ZZbsdZy4cIFLlw4z/Hjx0mqVd7y1rdy6cJF9s3u5cyZFyhyzXCYMjY6wV9+6q8wRmOsxRnI\nsgwcrK+v8brXveZlL9k1FTR2Oh3yPCfPNUWuKQpDlmkK4zAWdG4pMkM2zF8yLhmMK8h0Tg9LYYbU\nt7o8vrBIKwzZwHF+dYvJzHFoNKARFaROkxd9ijxHmwLrNK4o0FmG0ZpuL0VKwcr6Kt3OGhuXezQn\nKnzya9/hqStrpEruSkUkvlhKKRFKoqRECm8SEGWRxUElF+ROM9xosdkrkFqDVFgDWLDalg+j/+eO\nztYas4urXk8nt30aYwdojAsoAhr1KmCxztBsNmk2mzSqVWxe8O1Hv83m+gZLS8sEStFqbWNxjI+P\nU6kkHDx4kMnJSZIkIQgCBoMBAkGSxKytrrKxscHmxiYvvvgia2trbGysce7cOarVKp//3N9yx533\nMVqtsbm6SXyxT+hyRNZH9jvIXoua889T4SSZcaRW8MLlSwylYqAkFy9fxFh48OR3+OSDD9J2DcLa\nKHeGoxBoIhcxJODZXsblXkS+qNnbmMc5S+YGFGRI5RCBxYnrTzMMoMrmxpSTgC0LlxReOTHSqCLB\nj/hcLaLOOaTzXe2OyB/nO9vcaGx5OZIkoVKplEoZt4tX5kWOtY4sz30xc5bBYFDWipQ8yxn0h2xt\nteh02vT7fapJDNJ/uI4cOYQkYHR0lIvnz1Or1VBK0em0OfX00ywuLnLo6BFyq+kP+kipuOOO21FS\nUUkqvOlNb+SNP/pGjt14DCk8Jj83N4eUkgcffPDv3NxwbfNMhfAYqDVIwOY5URAgCoE11oPCzrf5\n3omhcU4QRCFSKKxwGFvgjGZYG6WoNMjWrjA7PkLFaZwtx2nrb1YYRJ4FlFc7YWP8w2J0hrCafDDE\n9HPGKhVmDxznzLnznJid9MQSVx1LosRIdzrKHXLKaYNUEisdyjqqhWEriQnQZNKgnCfDVBB4ILx8\n3oTw2zXNDoxxnZ3t+D4aYoihidQa5wzVSkIYBBjrMeZmvY5LB8xMT3P46FFybfDuNEWn3aHT7VAU\nBWfPnmM4HNDv92mOTfPQVx6i19kmCgP2zMzQaDaY2buX2X2zAIyNjKC1ptfrobVB5wHrK4vU0UR5\nAVojncKYApF52Y6KasRJAq7B1tIlnnnxHLMV2Dz/HJOTezj57DNkWI7vP4rNAlB9RggQGKQVRAzJ\nA8UT55b58dvn6W50kfUAGWgEEmMtUoK4/r6LAFhnMTv66rJIutLhVGj/4ePcpV0YwAv71W7TI0rs\n1GGQBCAcVucYIxDW7MJbRVGQZRnpYEChNVk+wBrnIbhyOgVfnKtVL6sSckAlrHtngc3oddp86s8/\nxvmLVwDY2NjkhhuOUuTF7p+72RyhWqsRBAHj9XGcNWyuryOPn2BqegorfOd8YH6e1naLqekpj+Wa\nghtvvBGH44UzZ5Dy5W/otZVGOYsZ9Ilj37ZHYYjVmiQRiFKGgYCICKULlAr8SGwFMhCYQmCjJhdW\nFjm8d57Tjz7KDx0+iDAFeaBwSIQxSCyBgOFg4PEX57DGUmiNDBXDdAg6o7O+yMXFKeb27mfx0hX2\nTs9wfnUZnRkIKbWuwVUBs7X+4SmtrMYYHP5BsErilINiQG2kSdFJUcrgCr0rhlYqAnw3ivBffadU\niQdfX+fg4VcQL27SvVgwXqSY585T2e6wdnmb/J7bqMaKC9/7Ds5q4uY4vbRNJW4iBKTDIbpkbnu9\nHpVKpfx4etZ+es80Rw7N06jXkEoyd+DA7qQQBAHOFvR6PVQgWN9q8RNv/in+4lOf4KAJ2NtIKDpd\ndBT5yUI7hLXgAqLRURCavUcPc6w9JN9cQzkYtgY4U3DnkSPIWoUrly9T2ACpYoYyJCoEeQwGyekU\nxi6tcLetMTK6h618HRVaFAGZGRDH19+9BDyUJiRSXoWxnHU46QiCgMnJqfLZB0oiyhq7q6Txj7B3\nJjrtu1aTDui3NhgMB6WSZ0ASK4IAao0qvW6fWm3a/wrWsrK6jHDCGyGCEo/FNx3aOf/Oacug0+I7\n33yI1//ET/O6172euQNzWG2Y2bOH1dVV/vN//hsuXbpEGIZ859vfYXJiCms1J0+eZHp6DyduOYEp\nDGfPvsCJm2/k05/+PG971zvBGb7/zCl+4Rffh0Pw3POnd7HaH3SuaTHtd/pEceR97oCNY4y2dHoD\nkiQmrlaJhSBLC6J6gqBA5ZBXRugjKMIEtbHKDfUaudPsPXEU0zboSkGiHc46pApQIqIYaFQkKYrC\nA+pK+RtcWJQDqyHNLHPH76AZCoLBgH0HmmwsTfH5b3yXn3zdHSg02lYoxXHeCCA8ibWjPxWAkJKg\ncDghCZSkoQVbuaVS1zQJfUG1YGTpMhEChCxxYny3er2dpSGJULTaK5w4cCNWCKpz46h4hKc3tlA6\n4L677qLX6/H1J54lrkiajSbb29sIoZAiYtBvgVO0tjpsbGywZ88ettptltcy5L4e2fY2o805Bv0U\nKSy1eh0pJe1OF+ccvV6XNO2x3brInj1VjrU06BwhHf20RxCGVESC7rUJrKEz6HF58TK10TpBEtA4\nuI9sUBAjObl8DrGwyPz8PHMnbsBu97iQptSXUlrVCs3MFxYhJGcW1rllboLhUk6zNk5Pr0KlICTG\nZNfnmG9f2jDAbubFjkHl2I2HSf+mT6XSwAlZxlwYQJZGRC8rdBac8MIpQ0R9bA/NZs76xhr1RoNe\nr0tRaLZbfmxPkgrVapUkihBYhPAKn1gF4BTaZJ7nkL7nNXrIYw8/xHA44LFvfIV/9pNvI4gEaWEZ\npEP27t3Lf/+rv4rRhscff5y/+qu/5OmnT3L+/FnW11s89dRT1OsN9kxNUYkjvv3Yozx/+lle88o7\nWV5d4eMf+4/cdsddXLp0hWyY8qEP/jaf/9vP/MBrdk2Laa/fIci8NEnKUpMmI19bhEAGEukUgVJE\n1ZBoZJpwfILNrUVGZcjR6ggyFmg7RLUEo6N1zhfbHBrUyMNSfuQcBl/gnNZ+HJdy16URBAG5LpBR\niHaOkVoFkWdUGlWG1hBEgl6asdEeMjOSlKC6lzDtFNAdh5TR2o/v1u52qkIIGu0B23NNxle7pOjy\nq+3KD3BZjEugX5Y6vuvtnE+7/OMjx1lcv0C60eeSbXPD9ByXtrfYOHeFZnOEVFU4d/Y8SZywvXGF\n558/x5EjRxgdHSNNh1RrCVk+BGEZnxhBm5zVy6vUxDbZ5houiLFRHzJPWo1FDYQUzB2JGWvUiGRE\nM9jHIGpzbO8NjH7+PEXfoZ3FYki1QQoQNiPPe6wttzh+4gTOGdykAGFZWmuxvLLGxa0tZg8eQweS\nwYXLnFnewoyOcENR5VknGIgYrCAII1qywV89doaff+0rkNsZ4/v3samvEEcGy/UncwMwpsCUms/d\nDAkosyUch48c3uFhfQPgPGQlSnzU20odzgofJIOgKHKcc2SFoVJr0mpt4ZxvOCYn9zAyOoIpuQSj\nM7TOCQKHM5qAHCEtwgqM1gRRGa5iNNvbPeIkYW1tnSRRrK9vUa/UiSsVKGWMKMl9972KCxcu8Pa3\n/yyf+tRfoFTI5z73GT7y0f8LazT/5g//gB//8bdw4cJZPvaRj9JLe2xurPLM06d41zveQ6M+8g93\nzF9aWvDSCAzGGNIsIzWWSpL4MQ6DFTFpbthorTMyMckNB49ysDlCNNqkc0CS2CpGS1Bdai3LlV6b\nqSBEOun1m0BmvIwplArwriahdjBTTeEs/WyIsIa8n5FnA/LA8tW/fYDB5gbhdIU///SX+PX3/jQY\nQ1AqCwRgisKPQGWR3sFUbfmUGWMYRpaqqZJai6qE2FzvPIFAKbcqx1YB/y9JyfVyREdz+pEnqY2P\ncenCGdaigv7jF2i+8l6CoEp9pMbi6iKXlpdImjVG5CSVkXHW1lZYuHyRbrfH2voqutA0m02ckzQa\nIe9/762ExSjSpCAEggCpBM4WOKtBCgqbI2yGMIbVrXWi0WkaImEsUVRVTH9riLMGbQypU8RZRB4X\nzMzUCYd9rA2xpFgJ881R9o9Nc8+td/PoU4/y4uXzHD90iFsmxthQff7FzFH+cOksiUxo1KuEyhJX\nxkiCSSr7qtSiBi4yxHmOYIugdOtdb8daU/7ldolSWzr5TC6QztHv94grNSSq5Jo8Pulj2GRZaeEq\nQXUVKkNAFMfkufGyJ2Pp94YIIUkSD+ck1RFPXOVDhmmG7LYYDlOssTSbIwhpscaSpzkqiOgOunzq\nL/8TN996N3ffdQdTU1NIIYmiiEHWRxWGb3z9a7z97W9n//59zM7O8fDDX8day+kXXuTchYv823//\n72iOjDHSqPPiE0/Q2h7yilcfZ3V9nfe9730sLCy87DW7tpip1gyGQxx6FyNzYUi33ycIFMLmdNMh\nvUFKpRYzWotZb10hz9tMZSPMhI7GyCRCB2yLNnOMMFpvkGEJrcNJgXMCKUJf2LRnyp31X78dq2m/\n1wPwBdZahsOUC6tX6K9tMWhvceLuV/Ld55+n0+0y2mzudpM7LqadOD5g1/Gxk2DlgCKUBJlmvdNh\nOhpBBgoKgwoVRptdSy2ACjxhdr2dmTtv4Ib5/ay3Wsw0qqRnFjg8d5Szi5sQBEhnMULwZ5/4BAud\nPiEZc/sPMju7jySpsHfvXl71qlcgpdwVdHe2F3F6jVBsg1A46ZBagis/tE7jNIQiKnFsCCoR0hiS\nwhFLQZ4OUM6SBQ6tBMPhAGkizNB6UbodoIMIbVOss8R5CmFAvwLxa6ahOksnj2mEgulggsFwwAcq\nx+kLicSQqBxnc2xmKejjXEGRFmSdDjIpCOPrs5ji3EsUNL5IejeUf+ajKCrRLrebpPRSvSk4LOxO\nWkJAUGZUFNrrqpWSROHVBgcn2IEknbMEKiAv8t2CPDo6TrWSsbW1yWA4xBQp2bBLvVohNxbnBF/+\n0gPsm52l1z1Mvd4oCWhLUeR89QtfYjgcUhQF4+MTjI2Pc/jwERxe9pTnGb1ej9tvv5X3vPvd/Mmf\n/DHPPPMMd911N5/567/mW498k16vxy/+N+/9gZfsmhbTbreNE8IDllagZIR0FicM2502joDGSJN6\nNUYFGp31IZWs9zUXL65x7m8fZtAvsEIQCoFQEq0EYRTyhn90H6995Z1MjI1RDWOK3BEECcIZBAJl\nRekJVygnUFgGnS2e/v5JNi4t89afegsPnX+WYT5gdWWDlcXz/NF//CT/+v3/nCAMdkf83aIKUH7F\nrbXIwBdqAQSFRQ4HNI4fZvnSFWZrsdfDmvJFe0mXqsLQs5TX2Vn604+zttmmMTLG98ZrJPNHSDcL\n8olJomZCe3OdJ777OHv3z9K9vMT6ep+trQ2GwwHgGJ+YYHJiktHRUS+0dlCvJlQDg8gc2o6jMUQi\nLd0sEudCcAKJJwWts1TqI8xsR4gLLexWH6slfZfjUgOFxbZy1o6Mk3ZaHNACGWi6wz5aSILckQ6G\niFrEqXSJrYqgKBRbLqKw20RJKTA3AudSjIM+CucEwkUomeNsgTaK/lYLU4HJmeuTzndO4tDs0vXO\n+SwFo8H5TOCi0FhnS2OCbyheKpTataMC4AkkYSy9XpupiVmGg4wgCDEWhHAEQQjC+JhMIIxCev0e\nSkEYJDjjG6AgCGiOjGGKIZU941w8fx4cyESytLbMH/7B75HEf8rs/jniMCBNUzbWFjj9/Fk+8tH/\nkwce+K8kScLDX3+YtZVVPvHxj7Oxsc4tt9xGrVbj/vvvRzvLd08+wdt+9u287rWv4z997GP82Z99\nnF//jV972Wt2zVc9G2uInYYgJLc5vYFD64JmvUIIqLR0PjnoZgVnLy2z1cswTuIIsNYQqoBCWKSR\nOAHGWB74xmM8/Nh3mZme5rd++ReQoff3B8p7wW1ufRdYWHqpplqtkFnD9qUX2bfvABfOnGX5yiVU\nqHju0W8yMTPDlaU1nFO7LhCrNVEYYsqwkh0bqCpzBnZG/0IbRqygP9SMzc8hVtYYqJxaEJJlmQ87\niTyzn2UZ119fCj/6+7/DypXLTM0fYHBuCVkd4+yVZeJqlagakIwn1C+Ocdvdd5KMTtDtttlubdHv\n95FSst1qsbG+ydTUNMNhShiG7J9roFyMFQlW5YSuD0SAwHkgHCMEwuYIm6OlxQ4V2Zktwot9EhMD\nEmVjXKBoB4bih29h/kdew8DmbH36ATovXiDWORkhvYGmlfTY0B3WZgxhBJGQSJuByr233Gl2nOiU\nxMpOIAim8P+tYrq5JhCCfuf6g2zAR+tZa0vtKBjjdu2YFq+GGG02UCJAuKtJas6VjigpykKrSnrA\n7WajgmCYDknTlDgpNalCEMchWWYRJULgnO9mtfG5HIXJcNaVk5v/vUZGqghhqNdrqHJKrNUSxscm\nmJ+ZYu3KBYbDlC9/7WuMTe3hp3/6n9FoNDl27Bh333kvv/Hrv0Gr2+F///D/xs++/e3cdOImHJDp\nnEGvx92330GeDShMQW5SDh05+LLX7JrbSaVSZCagNUiRtRrNKkRWEAlDF0ciAgZOs7WScebSBtqG\nBELgKHBqiBIB0hZoGSIcSOMQLsOYgqGJOL9wmd/90z/j1977c9RHIq9V1Z6JL4Y5Dvjzv/wr8uo4\n7W6X1vgIS09fZOP0aUxgmTt4iH7rMnL8VtZXTyOzIVSiXSxpp7DqogDr81etNZ7BlDvREIIisCSd\nAYN+gHEFVXywgseMvV7VWQtlUMr1dgYy4Pxah+pUwHNZhyNzB0i3W0zX6miRghQMswwZBBy74RhR\nJFlZWWF7u81wOGBxcRGlBBubG6yuriIEXDzf5P77Xus7UBS4wF/3q6HvRFoyVBIpIiY7EeqZFtn5\nFhWZEClLISyuW7AkMrjzVsSrb2IzyukGivjWo6yfeZEDucVazRDH82adzQlNI64RmcLrn4UlUDFe\nExsAnk32PnSDkhKlBFiBEIq0k1PkBhUrzHUYpwieS9iFm3Y4gLLAer5AMjIyRppfzTrdcUL5YdMX\nPIvZhb5CJSisQWcpoiEIw5AkSVBSoo2l3+8ThGEJeUkEhSd6C4NSUfmO+GYLIUFIkjhk38we6vU6\nmfY5AtZZlpaXiZVjmGms88HyP/POd/KWN/8EX37wQbZb21y8eIGvPPQQjz/xON/73klWV1c5evQo\nb3rTj7F/316KLOdDv/VbHL3xGLfccjOtVoc9e/a+7DW7tnZS5zux+niNiWqIG+RgQ4y09I0iDASd\nwrLZGnBxaQNrPDljXOkZtmUnKkAIvYuzGOd812gdQkpOX7rEBz78f3DvXbdy/5vfSBIqkjhEGcX5\nlVUee/IJ9hw6wdTsAbYGHcJMsLC4BEqQyBCjqqy+8AK9zjYXVtaY3TNRBi0onLHl2hT/hc2zDBWF\nftR3Eicc1Sgm0wWy0mGUEc6kfQ7HIRqzm2wlpEQFwXXZlQL0ZM6MUAzDgmYRUrRT6pUGcT2hs7zO\nRLPGFz77OUCy78Bhjt94hNnZWW655RbSNGV5eYXhMGVxcZHV1VUKrWlvdhHO7pJ6zgkfgAFIGaB1\nQV85KsMA8eIWxRNr6I0W9SAhTgJ0RZFFCuUceajYM3+ALKkibY5p9dFYxMQ4g1bG4O6bmHztK0m+\n+DHChWeJAoUKHKCR0qJFDSVzrM1RQYgUecmo+HAdKSQI/1FcXlpDqYgoDIjC5NremL/nSYfprmxP\nAFp7ojUoJYVKCn78/vv5m7/+NKbMIZbKF8Gds7v1ovTsK+mtpUWRl92nQSnftQaBRIaSOFHsBFhE\nccRg2MfoAhXHSCEpbOEzMYSkKDLqtRqHDx1kkOWEWY4KFNvb2wyGvvNVYYLOC6b27OH2m47jrOPE\nidv4gz/4PbTO+aX/9le45xX3EgYha+trvP3t72Bubo5LF87z5je9mUE65KlTT/OhD32Ir331Gzz2\n2Ldf9ppdW2nUsE9jpI7pDnHG/2G0884RW2JTFxY2aA+LUnzpSSTkLkrpv4K7Fkyxiz8KwW5sn1WW\nbSf5+slTrHfavP7V93LLoUMUOuBzX/wq2hhWFhdwwFT1ENvtPmYwIKpWkMbR6nRIohgcfPrBL/Nz\nP/VPmWoKivKPYoXwxVD6dH+s8yNHSXhp7TGgIJXYIKM2vYd4O8ME6urOKq6GTV+PBbXas9i5GWpZ\nQTE5Sq5zsiJlmEc4U9Dv9bj/J+6n3elyfmGJ7z7xBCvLfs+QsZYszQjDiImJCfbvn6NWqzK/dwat\nh1ipPYbnDFJEfgQ0IGSAFZLqVoH5/gbpZp88HyKDiCIfUkkilJMEUUjYrFGfnIS4gnNDHz68vM7q\nhUtUT9zEcHyEgYXX3v+zPHvqUc6f+R6SASrIcOwwzjlhkKKlxRUNVOBQ0uK0A2lLiZwkzwpUlJSB\nHNcnZrq8ssL09DRB4DkBm3u1ycbGBvv378doy8rKIoXOva4U/C404Uq99UtCTko+IC+KkuD33Wuh\nC4Ig2CVvo7BM8Dc+ZDvY2Q0nLGGgwAqwligIy2i+gsnxCc69eJbCOdIsJdAeI7XGK4SsNCRJwsTM\nOOMjozhbUKtUiKOQ9bVVryoojSHtTpul5SX27t3LyNg4b/2Zt1GvV3nyye+xb/8czZEz/Ny73/2y\n1+yaFtPxZh1ZWLxd3ZF5WsGPzIQ8fW6VfibR1mINJZgtsdYTN8IHgl3VuTm3K8qXSiGkt/U5LGiF\nznKeefYM33/2NBNTE5hUs7y5iXSSfNBldeEcWbfD0Ve8gjOuoFGZYG1rk8bsHKsLF8mGGQ8/fpLZ\n/fP81GtuwcQjROWeG2cMSiqKPPdOm5LJl0JQ5DkoiZYJ2nSpx4fJ8gXqjVEG/QF2ZwfWS2CD6+0E\ngc8c1caQ1OpsDwomJyZYXVminkT0+j3avSFpVjBar7O5vkyj0aDf75OmGW94w4/yzDPfJx0OOXPm\neZyDs8rxxntfhQgCBBphbRlt51/sMHMcXdIsfPlJRjeg7Xp0uh2GQDWpMT6IGCXCVCOqR+eRM1Mk\nzQbSVSEY0D67yE1HjtG/9Ub23XsPm6ttNlLNjT/009z5hneSthf43jc/S3fjPIldJ5CFH3VFSByC\n1pnHCy3+Y28KsiwidxFRUhBXAkRyfd7PK1eusLCwUIa1e0u3QNDr9Th/7jyzM/s4efIkUkhvO90J\nTC9DTRylvhNPwvpoQv9hMdbsklXVahWtPbfgw9ELpPJ5vsJ5ZYe1jmq1RpZphsMhjYYPlc7zf8rx\nLwAAIABJREFUjEG3x/Z2m9xYogTC2NHutkAKuv2MYd4lTkIqjRH+8Pd+j9HxMZKkwa233sra+hrO\nWgZZRpqmvOMd7+TkySe46667+eQnP8n7f/EXkdLx6KOPcvc99/DNb36dD37wQy97za4tZjrMUWGI\ndPh9SlJiRUGA48lzV+gOLc4KrHV+JYVXdpZRd1fF9zgobO6ZvlJ+gbXoIicMQoR1npkUiiz3afcr\nG1sUWuMk5MOMIIjI05xOq8sLj36b0dFJoCCpj1AMt5FBiLaGYjDg9Jnvk7/qGC4AYQukkAShB8qN\nNTgUTvjQ56IofAg0AZnt4IDK2hqb1Sqi28G4AOEChPAvnboO9z+Bv39xHHP2/AVmDxzCOv9xCUXA\nxMQ4j3/7uzzzzPeZmphkZmaG4ze9hSiK2NzcpNPpMGh3iSsJA1dQpI4iL0gOhZgkRGYNIp0hXYFz\nAtVTVJ7fQl5ukXd6xNsFC1sb7J2ZYi44wFJ7i25rAzUpCVRMNBqw9+7bsVNjFApUoRFWEa33iMaa\nrHZ6nHzgs0RBwqWFNUbP7OPxUydJu5v80/vfACOSvD0ANyDAIAnJ84Ef/03mg6F1Qmocm+02QoXE\nUYCSyd8p8v6HfCSujDv0ipUi9z765eVFHvzyf+Vn3vY2ms0K2+02sjTa7KShwY5w3+6uHPKxJ8KH\nQpedqYe4KPFnTZFnOGeohDFSSvJMU63WEPhMU+cEg8GAMAyJq3XiOGJjY404DmhtbIGokKY5tVoF\nYxxJEtFNB9g0J1aW1Y02vU6bxdVV9k6No/MBmYNvPfoYp597jp/5mbdx000345zjW996mF/65V/C\nGs23v/1tPvCB3+TgwYMkycvDNte0mAalW0hrD+L7ccCyPTQMhwZjLVhR1ksvv3hpgveOxs3vBfJy\nDeu8X94Kn2LvcGjjMS0ZCIIw9N2jVWAspvCef6MNTkKhu2y3O9TrVZyL6PW6GCEwGggUQbXKhaUV\nhgTEeoB1ASpS5LnHgVSpa0OUQLz1Kd87f15jHCEZ0ZHDVJ+7zEqkadqgZC8turg+7YdLS0vMzs7S\n6nRpDjJWVtY4cvAQNjf80R/9CaeePsWLz5/xIS9KMT0zyb59+xgZGSFJYm46eIi5Y0eQyhGvb9Bb\n36CpWkwuSHQ3Q290CDJNsN1mRMXY3hCdCTIR0qg1GDs2TafVph9r9uw/gDWCK6uXGdohY7XjVI4e\nIBKSwClQUKwsMiYVl85f5EJs2RrN6NBjcm4/py98jwN7IgbNPfz1Zx9kcqzJZK3GgbEaTdECNwSp\nfBI8PqRaWYcLJRvdLkEyQqWiiKKASmX8Wt+av9e54fAcV64skuUFxmRU44jqWIOjh+fYv28Py1cu\nI2zB3MwkKyubvnkg3H3md0K7dwJPEI40zxFAlmeI0s+fZxlhFCGEn+YsO8v4HAiPsxZFQZoN8OHR\nKSOj+7C2oNmo4mzBYNADY2i3OgRhyNT0XgaDnPHxKioJwGmG6RAl/WqUPMvQ1uFMwe9/+LdIKg3e\n+a53EwQBx44dI01Tj4Hj99TdcccdOAcHDhzi7wrau6bFVOxkJjo/vmljUNKw3tUUhcRSgJO7DKGH\nXsTuz/VnZ/Q3OAtGmHK88HpPDISBZCcVfNDveweUc7trEPzvG/p80kKRVEKyNEcFDmkUBocSCeN7\nppFRTC4Dzlxe4faDexCy6XdZSemDHsTVTnpnlXSeW5QxSBX4DaTFkMpCm0dpc1/epF2BMPejflyR\n1yOZz5PfO83v/u4fcu8P/yOWVruMj0+ztrLGgw88yHt//r089JWH2LrnlWxsbLK8skSn22ZxcZnF\nxWWsNTz1xBPkkeLuWPHaNKfe7nBkdA+jrZC8XgEbY3VEbCMiIFMJoqKoVKrEhSEfDmg0qrS2Vgma\nFYIgZmp2ls2VdTqH95I062grCYwgt7DyxPfZWDjNapIw/8p7GS26/IeP/hE/9Noa40lMnluiIOPI\n/DTnXhywLHPG6+NUwzaxBJzClhIgpUIvTg8KbrjlIIsrfSo1TRRbAlm51rfm73WkVMzt3+eJJSk9\nL4B/72668UYCCTcdO8zW8jL/5YsPYbRjfZCChkApbKlFhR0IrlxX4gQGS2EcOk+RoVdJOOeI4ghd\n5LjSxrq5sUJSqTAYDhifmCBQAUlcdvtCUq81CMOrYeojzQZxFNGsebjp8NHDPPHEMwwzQzXOd/HS\nKK6ggoAkDOi11nj2mWf51V/7V4BvhlQQ8I53vQutC7TWvP/972d1dXWX+3i5c211pmW3JoQCZ0o2\n0NFp90vpSxmV5zy4rcodMQK5a9dUO954IUp8p7z5ZmdVrf89giDA2qJc21zGMpQuJZClQ0PhsGRZ\nThAorANhNGGS4KxBCYXJjV+zYiCwIYaSXZT4SD3rdvNNKb+ySimEkARCgJNYpUjylInxfaStTYIi\nQbpywZ4urktv/tT0DL/zOx+mbwpqyQj9bocrly/yX770Ge6592Y+8fGPsb21TVFo4ihias8UkxOT\nzM3NEUUxs/umGZ0e5+iVBZLPfBZlNQsvPM/+xhjVQyEml35NRa2GK4OEpZE4YzA2B2sIlCSp1nzU\nnhCIIKQ5OUP9ntsRQuGkQJRaYJFrujEs5h2OxRH9YUG/n2OsXz8ehRVq9QYvnDlLGI2w0drg1PMv\n8po79xLHY7isi1QG61JULvyKDaUgFAQhCGEII//SX49HBQFOO4zbgZ9UGV7u14fo/jbj9TrNI4d4\n7/vexZlTz/H5h76OJsLoAgI/FQKla0oSBrJcDe1/WEpVplF5pYAQwudaoFlaWmTv3v1orf1qEwQq\nUGUYuw+ilkIQhIoojIABYRjQbDQId1YeGe+y3Gm8sixjtFajXm8ggEGumZAhxuRILC+cOccNN95A\nHMWcOH7CuyP7ffbt38+3vvkIs/tmd8m0H3SuaTF1ZYyWLgyh0KQuYL2X0k81hTC7DL7DEscRKggx\neCGvsxacI8szdkSHO8u/dFH4EFsESgVISflV8VirsGUqjnVojO8iS0Gx1QYV+909zjmUkLhCg7AM\nel2qlTq2GNLt9Ckygw0tDoUw3rZqjb36/lj/wOyk3OgSmM+jEJN1GJk5iGxtU0hHJguSMAJRLwv+\n9XWeffYZnnjicV73Yz+G0wIlHYtLV3jPe96FFPDud7+TdrtNt9vj0oXzdAcpnf6AO269hThU3HvX\nLB/48Ef5iOsQ7DtMu6uJJixPnn+R+WrKwakjyBB6bogKIqIsQBdDlPIxcSiJcJrGyDi99jpFoSEc\nQR2Yh9tvpFoocgVCGUwvY2FxgVN6gytCccf2JmdeeI7XvP4+VpYXQSoO7LuNfm+bleU12u02shZw\nuQV/89VFfvNf/iuyKKLXbrP84vfQ6bPsSTqMTE0i4pDJ6WlWLl9BRhVUEF3rW/P3OnEcU5RNh5QC\nEQRIIfw7aAyBGPGNTaFJKoq77rub2++5nXMvnOWbjzzG6vomTlboa5AyJsT6faXCT4RCePbeOYsS\nEmsdi1cuY4xh3/79zM7OAj4GsFKpIITyW3wFiLBCMxb0u5tEE5OEgSTNMvr9AVEUIcMAYyzffPRx\nJiZG2VzfQghBpRITRIKxkTqRchw+OM+lSxepVRL+w7//E6KkwqEbDnHx4kUOHzpElmU88d3vsWd6\nLw888AAf/NBv7656/0Hn2jugjAFpyQ2curBKZ5iBKJPtRbDr+0U4mmOTjE/upd3vginoba0hpNdp\nGl1q12y5u3tH32b8epKd4mi0w2vjvTZ0p6stirzsdFX5FQygdG1IKdFWo4Sk21lHSsVTzz7HXSf2\nEJqq76hf4s2XpXXOli4o8ONRajVBECK1wcoQ0dngUlYwrgqqKmZ0bAY3WisDWa6v88/f+y5arRbE\ndQa9Hv/2j/6Y5kid9/zcO1i4dImPfOSjzMzsoVavc9stt3HsxHHq9YjbThwiH/aZVFX+8fHbSF58\nDvbNMyYs4xvbxDXF4vnLTLg6NCpUnUaHYHREPWhSFAVaCaSxVIOIltCMJE3SfkrfKcZefTfdaoXJ\nTUMRGVKRkXUHPHX5BRaDnN5QEQQKpQTGFIyMNjHGEihFo9Hg7nvu5AtfeIRaMkFraOl1BvzL/+Xf\nIOOcihLMNnPe9Y67qIdDbKD9ahwr2D9/iPXtHsjrU2dqjCVOfK5sp9thtN7cdfQFSiGjmMJoht0u\nUoZYoRAy4sabb+PGm28FV7C6tMiXvvgA2gR0rSR3Hr/MslJqJiTDbof1zQ3m5g4wOTVVEk2+ubFl\n4HqlUgEccRwRxzHt7S0OnDjIxtoCcTxLrVrZlVV1+33Wt7YwxnBgbo5Ca4pyW2kUJQQqZKQeYHTB\nMMvYOzWBtY5z576PNo6Tj9/Kww8/wgc+8JtY53jooS/zo298I5OTk1hj/AqTlznXls0vNZbaaM4u\ndtgcaoSVhFIgrQDpdkNEsjynPxwwoWKOHD1At99itF5hu91n0OuRGetxGSUw1uzumZFClriW36lu\njN29UVchAocKQt+JBgpjNaEI/b+bAiEclhBhPMYqVMCTp06y9ab7mGjGfnS04JQuBcoOSlJMCijy\nnKpIqBYVcqfJE400GcGGoT5/kK32FlPzs1w8d5mRlSWsUBy69cZreWv+f58gkFxZWmbPvnksGYtL\nl7jrrn/CqVOnePgbDzMY9FlYWMBay/LaJs+ceZ5f+aX3oEgxusO5yxcIF8/QDBJMr0se9AmtxoyN\nU9x6nPCeuwiqFVY++wUaGxtEddguLhEiCazECUUvy6gQ+A/iSJOF8QrJ7fPUBpbN0Hu+tdGcfvEU\n8XSEuChojkW4OGJxdZkbbjhIu93BWseemWk6vQ0aI5MksaC3tQ2xIgktNdWnGHaZ3VfhF979KgLZ\nwqo6YVBBOYtwEU7GJNUqWXF9YqZRFCHwo/ro6CRC+MWFaZrSqNfBWCIVMFQCEZT5CKX1FACnmJk/\nxHt/5X1QgOl0uHLhBQ6OBlxEs3TpHFluaY6MsX//foQQJElClmVX09fwa7p33lsP14WEgSVL+6Rp\nn16v7zMCyrXxhTV+LXgsWVpeZmxsjKIo6A8GOCS6sOR6R2EjcEFAJa6Azen2B/zZR/+U5bUt3vim\nH+MbX/8ay8vLPPLIt7jn3nuBl3I1/99zTYupNDlWhpxb3GZlM0MJj2da69PmnbVeRoHvINtbm5zu\nP8mN5lbmjt2ImT2MuHCGRpqSdlukwx7tzhbWGKqVKgPXLzHUAJz3T++4MXCgSyzVWN9Fel2qxuEL\nsNEFhdZYY4iCgiCqkBcaKRSDIuNLX/4m73nrW0EXSOl3wwdBgHEWJfylHaZpuX7F0asPiAxUTI2i\n0mD0wD6uqILeekbrzDmmpEBLQXAdElC5LqiNNnBS8Mij32F2/z4OHjzI1sYqP/IjbyjteC36/QGn\nL56n3+kxv+8g/e018oHjS195hKy7zUcurfPDYUJNBLRqEfO/8vMkxw5xGYlOU/IjM5z9+tfJEsP4\nyDSNSo3YRfSqIamQYDSpSRlKqL3mtUTNBuBzGYoiQ5uUsFrjlntfz72vvx+rFBNTe6iPjGGcRKmY\nOAxxqaFZnaU6XuVf/PIEX/riF3nxXBepApJ6n//x/a8lUAIVZkAVIWMQzludbYR2FWqNKfobg2t9\na/5ex8dA2qvS+1LcXqn4TrvX6RMnPt1rdzV5afF1ZVavlAHOBRA4grGE+YMHWW53GRutMTdawbqA\nyvQkm622157itxR77NTswgECiOME8FrVWrWG0Yaxeg076FI4Qa1apSg8a5/luYcFlaLQPnfj4IE5\nlpbX2dre9itTtCZJYvqDlEIb6om3o5ui4M477uTGm27ikW99k3e+8+ewxrK0uMjmwUM89tijvPof\n3fsDr9k1x0xz41jd6oOMkc47hnZCFa66KHz8vHOGPOuxvHgJDRy794epNsYZsk2eD0iUoD/oUWQ5\naZqWI7orIQNZhtl6XFW8dJQWgt3NoKVu1WMj/udaKdAmIBSRl3pYg7GCcwtL9PsZjTjECYsQwe66\nZ2sMWVHs6tKMdCTaIQkomnXqB/exudXGDnrMJDEm7VKEAWgo+LtZw3+IxzpLpVpHG8v4+DTNsQlE\nGLCwcImHH36Y8+cvkuc5IBmf3UMtinzEmpWgA4yOGZ8/Rr86SdpJGSUinB8nuvUGT+r1CvIiY7m3\nxfz+/ZgabF9s0WtndIXhCil9pSgAoR0ujHjd6BQ7RGOv12Vzc4OsGBAmFU7cfi+VehMRBGT5gHpz\nlH5/QLM+wuWLC3TXco7dfCudrRbNkRHuvutezrz4BZTp8epX3oQSQ7+rSCQoFSJMBlJgUCAF1oFx\nhujv0CVeV8f5v+3E6dVHmlxZuMz09OQuQeTK/FIf/7ITbOJXP4sSIvjX/9P/wG//rx+mNlpne7uL\nTNu88pYb+NapZ3FSAdIv5CsjM0WJsYahx2xdmWLV6/apVvzWi8FgAGWTFAThbrg8CHTh4blASoQz\n9Ac9mo1mmZNa0Ol22btnGl0So7VKlTvuuBNtCpaWFnnLW/4JDz74AMePn6BWq/HmN//4y16ia1pM\nBzbgO89dpLACSBEq9GyhMOwswnLOZyQqofDbLnNaG0tsr6+AK9h75B6m9+7l/Nnv09pYI4wqFGlG\nVmT+C1dKkow1ZVEVGOc7CCnl7kixs1p6J1ncYyOWvMgJwwY/9I5X8eSXv41IJSIMIIxZbw84v7zG\niYMzZHpInaRcZQLS+VCMoih8CC6WibnjVGemOH/xMtlTJ6moiGZSIR6fZHNtnepIwztFrkcLohAE\nUYX+YMi999zFgf17+chHP0ry/3D35lGa3Wd95+e33eVda+2tWt3qlmRtXrBsI4yNWMwaBwP2ybDk\nHMBsCUuYAMMZZiaTwCEESAIBDmENDksgECBgExYTjI0tIyPbsiRLsqTe96qu9d3v8lvmj999q9sB\nMef45EzT8/MfXdUuVXW9973PfZ7nuwlHr9fnbW97G6urB0hMSmuhS0tYQj1Epo7xeMyHX3iSceHx\nRc2jSvP6e+7jG7/lGxhmBoYj3N4AP9kjmc7opTmJTugeU1yrBjzntgh3rSLrkipAXaX0OgdZe/n9\nnDp1it/6zd/l1KlTDEd75Lni4Ycf5mUveyWv/fTPoNfrk9kWDz30ML/+a7/IQm+JN33u5/Ph9z3O\npXNnOXT0IOUocHDlAD25wxd//j08cHcaXaIabjABpG7ta9OFaBO8BhdQt6k2/6+decPBDXf9o3cc\nxdbVvidvkhisjdiDbN7DQqjGWQuWl5fJW5qFhQ7f9X3fx7ve9cf0F5f54Pv/gjsXW9x18l4+9sxz\nDGobx/vgKcspQnlUaqiDY7C3Q7fbpygq7r7jACaU2OtDfLMGMEY3U0hNlilmsxmry8uMRiM67ZzL\nV6+xsrxAWdX44Nnb2+O+u+9kMplgK8twNOaRz/k8rK2ZTmccOnSIJ574KN/yLf+I4WjEo48+ymc8\n/NDf+BLd0mL6oefP4X0LIatI+I1IEeEmUj4Ql9WyoR0hm1HC8+LTH8V5wcNveitHfIU0KZWQXLcF\n5aCIT0wRPRX3I0aa7xmpE47UGERDv4j7VY0UGh8srrYYFJ1DB8jX2tx13wme+egVvPdkLuBl4Nf/\n+x/yL77mq0k6CbUrmBUBkxhMJlGkaHqUhw+gRcaZ82dYuHCBBWtxrRQvJdiKemcXc/AQlCUiWGy4\n/TpTW3uq2nL1ylWyO4/x3ve+j+sb16GecuXKNR577EMoZXDWc+TYYX7ih38QV3ucDWztTPisz/ti\njp+4h72tTQ51e9x34i704gKVHRPqkp3BabbPfIILz78Hed/9dA8dQBy8k1aiecjusFddYjq2JOYE\nncVXInSboDR/+N/+iPe///0oJQHHyTtP8uzTH+PylXUm5Q6vfe1DHDlwL3fdcx9f+/Z/zK//6q/x\nS+94B1/+pW9FoBgOd3GDCX/27nfxZV98gmNrYHSBku3GWzMm5UqVN1Q8hQ85IUQtuXe34YPxbzlB\ngvRNeRTR3CTPc3zwlEXBdDpmYXEx0hpFw7IJ0bOgt9BlOJywsrKKlIqinHEshX/xfd9FqQSzac2Z\nq5dJx1Mefu3DnL16jQ89/ld4W2OkZFaVWFuzu7NN3o4rh3IypZdnBG9RKopjqgoSreh3O2zt7EVF\nVFWgtSZNEmZFiRAxbrrdahEa4VBVW2ZlwaSYkdqaRz7nsxiWY770zW9ma+s6Sikmk8lLvja3tJh6\n22LO+ZybzN5Mxt/Pa2LuYQjMLb5iUhfnP/EE0iR0O21mezsUdYVAo7XZ58Z5B0GqfW7q3AFfypjj\nNF/2eO/Q8oYlng8BlWWsn7vMXSd+hI/87vchvcU20dFVKNnasYxri5x4EmFIdI40hiok+P4Ci/fc\nSfXk8whlWVVReVUnLaR1SC32eXYikbDncGm4eVN125zJdIYQmqNrR5ACdnd3aLdbvOYVr6Hb7aO1\nQSvDZDbjiY88jnCBalYznVVcW9/j3f/9vZTuvbzh0x/ijf/wa+ivrLBXTJgNdrj64vP8+Z/8PAeW\nOjz4ls8gPXCAYmGFZWvwXtIJx8n9G0m6AmsNaXqQIGJn+BVf8TZA8swzH0cqOHXmPK1Wzv0vX6Nl\nllC+h7MCbXocOHSct37lV3H21Iu841f/A1nS4vDhg5x78SnuvbtPt1uBEjizgBG24UZGg5sgAi7E\nkTYI8EFha4OtbvWV+RTPDZXM/qfz0VkLgW9k3fvTHIIsz0nTSMK3jZOW1orxeIrWhqWlJRYW+ly+\nfIXJZMrRo2t4qQg6IUGS99ssLi3yjd/73Vw8e4HL65dp5SmZSRnsbpF3e0wmE44dP4lS8PhTz3L8\nUJ/K1lRVte+HoZTCaIUkkGYa72qc8+RZyvLSEmVZkiSGoqw5eGA1rgmAqq6oq5rZrGBzb5fXv+EN\nEAL33Xdfk9PmeOSRR17yJbu1CihZE9MMFVJ68GGfeD/fd87tuG5QjGiI1fHj2mnOfvwvWekvsXj4\nKPnBg3SzDsWFSPyPdKjoTuNvKso3DMTDJymOfDOeCwFW1HTX+jz4ujv4o1/9MWZnrjIUBqwl7eaN\ngiuwNRzR67dRSjPpZ4TFFWzeot7apPWeRxGdHJPmmNDGeoeREjdfYQCIQH99zLNhzH2qgy9uP9b+\nxqUN2quLdDpdtFJ87MknKWdT3n31IhcvXkYISV15kjzl+OGDiCAwKqHTXuRld93D5z3yCC9/5Sv4\ntFfeR6vTQUhwTrC0dID+K1v0DvwflNNdugaMbtPpH8GpTiR1O09dC6wROOG4srnN5uY1Wi3FvS9/\nNW//hq9jfX2dj370CX71V36Fbjfj1IunOXniHopihtICLQxJmnL02DF63S7T4YD3ve/POHPuFK+8\nf4G1gwYT6mgR6aYE1cE6EEpgg0P5GUG0omF1oHnggrv9hgygKZyfpDK8gWCE/aYkrt/msm2BAB1J\n1t5H9ZBUkixPydIU8KRJm+eee54QAmtrRxgPBg1rJuZODYcDdnZ2OH/5Eg99+mu5urXO27/+63jl\nq+7n+//5DzI6ssZosMes6YLvPrZGWY9uGK4QZepKyqbAGgaDCSIElJIYo5lMp1BDmpqGbWPRqabV\nyuh02hw6cIBf/L3f4Vsf+E5OnXqRv3jvn/P1X/928iwn/C3RwbdYTmr2yffBB5SaL67jaDDfzUSt\nbrRKmCeBKtVEygZLcJLCBurKYsOA67sbBALeeZzzIHzU+QswJkUgKasoW1PGAA7VaKyDFFQugklp\nYpjuTGkVGc8++igypARb02p3m8iGgPKSJ8+c4zWvfIDuy+9g9+I21eZVetOSNlC0UtIQqKxH5IIg\nwGJRKv7ulXOoJGGiC44vHWc23uL8cMLdt/LCfArH9gzjjz1H/zUvZzAt2bqyQ+4tK3etcfToce67\n7z7uvfdeZnVgtrOJIiCCwFnPgdVFXvXye6nrCe1Uoqnx1tNKEoQ2ZP0l7ltZw1FS1yPqUY0OORuT\nEUXh2Ni4zIULL5C2llASXF1SVVNeOHWJK5ubfOEX/D3uuOM4Rw7fgbPw27/921y+dJV77rk33nBK\nc8exNdI00ph81/Oqh17D008+hmGPI4f6LPYVJvNYX6FcgfOt/U6tobYjRIIPBhcSai8oraNyt5zK\n/SmdG/QkaLqO+PfeN2F57MullVL44ClmBUmaxBC7NGnA1xBXHsBdJ0/wF+9/Pwv9BRYW+kwmEy5d\nvIwLIvol1JYkybhw/iIXLl/GZCn/6Ju+jd/5r+/kvvvu5vM+73NJP/Q4xXTKuQvnqY3hE2cuk6aS\n4Wi6XyusrdF5i0h+Eg3bIFBWNb1OG+8d1oKQFiUgNJ7CaRrH/tpZnn7qabzz/OmfvJvP/uxHkFJy\n5sxp7rnnnpd8zW75lZYCRAgYnSBVfGvW3lIHccPViyhHmxdSMSfbc4OGMZuO2dq4hkgkOpGk3R7D\n4QDgphWBjKF6MsQcJ3fDBRxkfEISY5xVQ07Gej787o8ifXzymSwnyIB3FYnJcMGzfPROFtfu5Py5\niyyXDu0lQmicCvi6AZRchapN/FkWgheNq7igFDVdJ5jISM3oHV79//AK/M85HZUxGnty02FXW77h\nW7+R8fUNNrY2+MAHHuX8+Qt84AOPUli4//gRXv2yE4ydR2YG62csLSesrq7FjjQ0AZd4tJTUIZAJ\nBUIz8Z4CT6ocpR2zsXmtCeBLmA42aXdazMooLZzMZgwGO1y5comjR0+ileE1D72ORz/wQcoy7t/2\n9nY4c/YUBw6uoJodaJrlKKM5eGAZ5ddpZQKtABmTLpVsxB03jTQ+xM7MefBBgDCNG93tKSe9+czX\nYn/bvFTMCtI0aehMTdPTOEnNX6ZLl66wvr6FVJJ3vusPmMeYjMYj+r1+DFNMEnZ2dvmLv3g/X/mV\n/wv/+Vd+mX5/kZ/4qZ/hsz7rc3j0scdRMnaYQig+/TM/i2ef+nAEfhvwWGmDxyNkaFZPVXvbAAAg\nAElEQVSHsU5kaSuKKoluVVVVkafR8aqua/J2RppF3+I0TXHWMhqNePDB6CR16tQp6tryxje8/m98\nDW7plRZCoIVnsaXptxSr/YzVhYxuNq/xNxB3Kef0pRuGJ7FzDQjhKasp49Ee471dhjvb7O7uYq3F\nOct++udc6y9iyx/doiwCSZLkKJk0XbJHSoU2GqEkvYUeQaRYo5CtJOqvlUB6R5Yq/PQ6Zz7+GIvr\nO4jZmGArSlnjlMPXBVU5I4QoIqjrCojhb9bW0Suy0gQJamuD7anjoLz9dqYjmXH0M1/DQAWeePYZ\n/ssv/Ed+4zd/gw996K+oqoqqKjl+/Bhve+ub+cIv+QIq4ampGIz30Cbh0KHDLC4uUdeOqhndaUAj\n6pL1rStcvnSZZz/6PM899Tx7e0OKWUXA8/SzT7GwfIjuQpfzF85SzKZcvXKFg4fWWL92lQ9+8AOc\nOnWKyWSCMTkPPPBKlNJcuXKF97znPVy/vsF0Oo03dxAkeY4xKa9+1QOsHVQsdBVZqtFpB5N0yZN8\nn0rnXOQhuyCoLVincD6lspraaqy9PYup9AEVQIX4sWy8hudHKcX6+jq2IcC3Wq3oydt8jZR//ff+\n7M9+hH/wD95KURSsHVnjxJ13cvnyFaqywnvPlStXefH0ad74xjfQ6XbY29vjVZ/2EK99+LXoJOWH\n/tUPM5lMCUIxKy3TWcE73/Xf2Nge0+50qV2ISiydEISJf4ZoSp2mht29PbTWlFXNdFayt7eH89E2\n0aEZTyu+9C1fjveBr3jr2zh37ixra2v7uI0Qgne+8/df8jW7pZ1polOWu22W2oYgIxKnlSJNuwym\nV4kbkDng1KiTnGsMUuLYH0f/yH+z1kbXGqexro70lbm2uDFCURKClCCb7jbE6FkXphiVYHSG0Iqg\nJQUSrQ3FeEbtapIkQyGxVYV0kGhNXVqGmzuceOBeprMxuczjOxBwlUYbRaAiFAEtG/d1DzJJ9kcp\nYyRF4RBJYO3Encw2rgDHb9FV+dSOcCWnXzjF4XvvYXFhgf/tO76TPTHlmWee4dSp05w/f54/efe7\n+egTT3Do0CEeeePDfMnnfw66itSauobZtCBNO4RmfAwhyhrH4xFPPvUR1tc36LQX6Hb7zIopTz31\nBNoIut0OGxvXMCpwZO04u7u7tFp9nn36GV7+ilfyzLNP0s47PPv009x11wlGw3VEqDn74mkOHjrC\nx5/7OA89/JmkrU5jMO5R0kKYcuRQl0wnmDRHpi2SJCOREqd003iJfRQ/CAMkeJ9gbaD2t6dpzd90\nZNNt+hCBWmMMhw4fAsD74qap8Ybr2Y3fPbJzWq0Ws1lJWZZoZTh44BDtVofJeMav/aef5ju+7VtZ\nWV7mR//1v+XL3vxmLpy/wF89/hF++7++E1tbEpNCI++GgHMWYwxV5SjqmnI6xWkd3aekoBw62nmH\nleVlxoMtilnZvJ8mjfGRZ2t7m3Y7RynN9a1tjhw9SjkteOSRR/j5n/85Tp482fxugs/+7M/h4sXL\nL/ka3dJieveBFr22JjMQUE3AnGJHCZQQOB8a/fwc1Y/FMMpMI4Az906MhPvYzsfFtyC28/6G241o\ndkFKoMhIFYxszBlS1mHtDJlYtMxQIgFvsbYmUTeKtoD9J3AlA7qdYUXFpWtnWDQtlAKr4k2kTYLz\nEu+j92UwEumiU46rQzRuIFCHGPEsAHn9GsrffqhF+/w59LVtTl/d5Mf+5PeZnbqE6CqGwyFVFcGI\n48fu5I5jd3LiZfdx5z0Pcu7SFV521904XzOdDplMChYXJCZPoyVbklJVFdeurXPl6gUEip2dLQ4d\nXOP0qVNc31ynKCcsLS2QZ11GoxF53mJl5QC7u7scWTtGWRYcPnyYZ555ktm0ZDjaJssSDh06yHRS\nsHHtMq9+9YNcX79Ou92jlRqEs0x3zzC4/mEWugbTbSGVJk1TtGkTZEKqoxt8NJmX1CgCCTExV2Bd\nwLrqtmRmAAyGe3Q6nX2utg+ePMv3VxtiTs4PHiEFIjRIupQN3uEbEAtCiODVaDTCmJQHH3yQnd1d\n3vCGz+TM2bM88shn8Zmvfz3/+Td/i9FoxJe95S1cvHCRf/dTP8veYBQnTO/pdNLoGtVMp2maYbRG\nCMnGzpCVXo+iiJRILySVq2m1c8bTPUrrmJQle+OSynqKqkBqSW4SgjTMipLRaMxP//TPkKRtvvpr\nvmo/Cr6ua6bTCb1+ny/4gi96ydfslhbTO9eWUTqCMsFF9K22llTPWOx2uD6YNEvwaNMXQuS1xYsY\nXaDmS/Lg2S+qwUcT5mgarfd3rCGo6FkqNJ2DitQpZrMF3vz2r+BPf+rXqKsab2tqHwEoRUVla2oV\n0LoFQjUotKbGUkvIpURXE5IqYIVmPBmQJBm2rtE6Jc/bsSAnCd7Jhq4FtozZNCZJkCJEbi0O5RXV\nbcinSaQge/NnsDKFz/Vj7n1LD9XNcNZjreeJJ57k3MWLTIqK97znzzh16hN8+kMPcuzYHSgh6PcX\nmM1KppMZnUSTZdl+zs/W1jqDwQ7Oga01w+GIy5cvYoxkc2vA8vICITguX75CmqasrKwyHo/Z2dnF\nuQ6tdkKnm1HXBS++8DwbGxv0+8uU5QyTaD724Q+Td5dIE8PRtUPIapvzpz5Ct5vQ6ghMmiOkRqsE\nJU3znoqacZOYxlwnw7sE5w21ddR1oK5T8LfftQRYWFiI6HigcWsS+/ea3F9DNdCbkE0Sxnx33Bxx\n44+Gi8OP/7ufoNvr8dQzH+fxJz7KD/zgP+fDj3+YP3/Pe3jLW76UK1evsre3x8+/41cYjCYIwCQJ\n7SQlTZNobGMdSIFQkqqJ2M7TNkluKKqSqiwweQ+85+qV69x951HaqaabL3FtawunNI7AkQMrlEVB\nWVmUciwsLHL3ybt4+rkX+Lmf+3nKouCxv3yMs6dP0esv8AVf+EUcOHDgJV+zW1pM8zxBquhx6ACR\nJI0BbUKvnbE9nGDnUbmykYSGuG+M58YONf7p9x2y54LUuQONEPM4BYWSmode+2qef+Y53FSStDtU\nVR2vfRDREKOc4rDRIyB4vLLIEONlpVLoxsikKkrwkmJaoWQVJZKhgkTiXWgCwyTWxujaEBxVFUUI\ndRX/TdpIpFckKsfO/VBvs3Pwvru50hKM94aYdspTT3+cUTnhA+9/DOcCVVWTd7u02y2khKXFPgeP\n3EHhKlpphqsDeBkd0IlBdM5ZitmM4XDAcDTA1oHDh04yGAwiIuviTnw2m6FUgvfzULc4pYyGQ3q9\nNuPxGGMUS8uLPPvMCwgpmM2mmERTFQXjyYSzZ17k2LFDYLvMRtcQboxOAkJpjMka703TXNMbPqVC\niOZBGCWkIQRcs54QeKTy/y+v3N/h0yD2NwNt/6PRx82fSxGYY71CyH0/07n5j5SSb/6mb+Db/sl3\n8dVf/ZV8/pvexDMffw4lDcPxmBPHj2N0wi+841ewTeSJIOZE1XWkN85FAEYlSKVItY73oAhkec54\nNEQK0GlO7ccYkwBxUui3UuqtHaTwjKcTzpwdc/LkCVKtmU6nZFnGcDTCe8d9L/808izDWcszzzzJ\neDSkrhxvfevbXvLlurXFNE1iNykllaqxFpTUGCc4ejBlY7DHcOwJNKaxTbjXDQR+7lJz46LNvUBl\nE+YVo0LiExYVKRq2qvnAn76Xz/mi16JPb/MnP/0OBA4nI/rqfY13NprRCoFJEopiTEILEdq42iO1\nIFEKLSSTyYxcZsjpmGJWkSYZaVpHxFF6vIdW3sUnDoGgqgvarS6udthqisniHjjRGU5apL/9Fm16\ne4aeBjaGe/z2f/kt8t0xSZoyHg8ATa/X5+HXP8zK6ip3332Cu04eZzIds7c3InQDXRPd0ZNW3nSl\nirKccvnSJc6cfpHl5WNkSYcQBC+c/jhS1rFALi0wnmyTtwTtdk5dWa5vbFKW0QdgPB5xffMSJ++6\niyxL8N5x8uRJzp+/iBSSylq8knzsyb/kFfetcFVsMN75ON12QZYmmLSHVCYWUxRJ2o7GOUI2ETWR\naypI8EFjvcRaiNYOFcYUt/jKfGrnhmBm/28+aR8avyZ2qUrFwhkaufa+bWbzRaGRoU6nI77/B36Y\n7/qn30mn0+W7v+d7eeCBB3nZy+7l8rUNvv2ffi/XNjYYjadkaYoAOu0OdRUDNGfTGXOcJHhLkhpa\nrQybRJphliZIIUiSJK76pEFrxd7emJUDixjTQvjQ7EA9Acm1a9fotlqUZUm326VsdvjPPPUxDh4+\nQpa3uP9Vr0NJxceeeoLzFy7w97/0bx71b3lsiTYxf2lOtNVax8wmLC8/cYSPPHuB2scLMl9ySzlH\n8ue+pbErFUSDBLhBxm9qbkPV8OAiZWM2Ubzvj59DSkFdzhDGoEV0y5/vXptvH/d3WuLqCtdk1viq\nRjTu/eV0j6nQWNsmMTnOVdQuxtFWLnZN1llaWR/nHUmSACH+KQReWpxzUU/c7VGMRrfoinzq57k/\nfJQNUaLuO8q3f/3bWWimgb29Aevrm3zkw09w5sxZnn/hRf7ifX/OseNrHDt2B6/+tFeghSHtJiRZ\nSqfXBTyzYsLW5nWefOoJdnd3qew2ickYDscIGaiKEf2FLqPRgKN3HKQqLZub11lYWGQ8GjMcjuh0\nc4qiYGtzyMrSjP7CAifvOsre3jadTpvB3pS6qqLl3oEuq72KjXN/ilAVCz0TVXQyJzFpLKCoaOUo\nFDb4ZiSK5hygQCo8ktrGt5mUHpPcnmP+HByd+2PMM9bmjcwnFda5Zp85L1w2E6G40cwIwU//+58l\nz3N+7N/8BD/5kz/G9//f/ydGGX7/nX/MZDTl+tYWeauNNglKa6RSN4yDgsc6G32CgweZRGFEEIyn\nM4SA0ua0u12qYkatDKH2SOdYWlyMgZ1KYXRK2upRVZ7ReIKSFalWpGlCu53x5FNP0Wq16XT7VLMx\ndTFhd2uDurZok3Fw7aWB4VtbTLVEKAnBo4KICqUAnSxHCcchJXndy1PWd4ZcuH49OsBIgw+2kY5J\nggzgPUoopBc4HEHM9RrzpU3jLyriolxJhRcwK2bNXi5GnDQwU+PM7wi+iV+2Hpkm9PqLlMMpPgSy\ndopD0EklWRp3qVVdUdU1iTIYo9FaM5tOaLc72LSkqgqU1NR1Rl3PyLIWxqRUrkaomp4xlIMh86TS\n2+msfdPf5z/9X9/PY3/waygCnXbOcDhme2unKUSCYydeRivPufPOe+j1OywuLZGkbcoqiieydo5Q\n8UHmvWMyGTEeD9nd3WNxeRkhAu12HmtYiMm1WqdIabi+scX29jZZFjODAqH52KF1wmRaIOWY8ahm\nZ2vEbOYY7I4ae0bH/ffeyebGCyy2xiT5Kkke10FGp0jVRkq5H08jERhSAoEahfMJtZeUTlE6qF0g\n+CgGMrecyf2pnX3FYeMAtX8rzXenTTrFvGg65owZedPkGM2JopIxWvh9/de9nU67Q5Jm/NIv/zIv\nvHiGj3zkY03EucBZy+JCfz+rLTTrFNkIBeags/ce7z1Fcw8bo9nc2mRloYcimsAfWDqIpCTv5Ozt\nbIFcYjybMdzYwpiUO+44wfbmNZxztFstjI55cMPhkFlRMRwOSYxh9cAq/d4Ss9mM55/92Eu+ZrfW\nzxQI1qOlQoeYTDh3mdda0EJzeDFnpZ9zx+oiL168wvXRGONUA0jFAmxMZAKUMuAqjwgQomBzn6Oq\ntQYfR5Hae5Q2iBBwdR0724Y5ELmGAWOSmJra3EDd5QOkrQ7jvSF50oo7U+9YyAx4j9MaW9XRK9Hd\nxG+dO0clCWkr2rVpmdJutyjLgjRpkWQJQqf4IDA6pbK332j43LOf4PzGLolosXJ4kRMnTrC8tMTx\n48dp5TlSCJ559gxXr15hY2OTZ559BqEkFx68yqGVZd72ZV+CNgohJXVVUJUF19avcO3aZcrKMR4P\nkFKweX0PQkqeaVqtJZR0TCcliclptVoQoN1uMx5PuXr1KkvLCywsLOKc4+y5syiZMpvW+CBYWFhg\nMnUIRqR6SJo4slYHneRokyClRukMrZMbfGcRQU1b+wiCCBmJ+UFRW0HlGgNyIuUtMemtvjT/804j\nwY4lViAbccWc/z3HKGCujoL9Kgx0uwv8wi/8Et/6rW/nHb/263zwg3/FxYuX6HS7SCUpi+j0NhqN\nkFLS6/WwdR0NoxsTE6N1/OmioUR6T5alTTYc5FmKF4HTZy8y2BuzstTBaMnKwcNcvHwNjyTN4wN5\nZ2cT6wO1dwgZECGQt9rYqkIr1ZgfCS5fvoySEqU1q6svLai5pcXUyHmInEc2Js1Syth5KhkNS7xD\na4FPNfeeOMbCcIRwsLM7oCwsQgm6rTZlWbJbTKMFWx13IiKI/SdZkhiEEtRVHLtsXcX8IBEL8nzv\n6hti/9wt3xPI2i1wUIynZK0Mj0N4gSSwkOYYo/DMaSHxqT1fQzhvqYsKHyzW1wipSJM2PtTkWRfv\nPNZqZJIhlY50rLq+ZdfkUz1713foLi3wTd/ydrJEcG1zk729PR577EN84rnn2N7eZmn5yE3JBxlS\nCZSCvJNh8hTvAzpEGXBd11y7eo3xeIz3gt3dnebhmCCFIU0z6irKcne2d+l2OrTbbdIso64iD9K5\nhMFgQF3VtFrtBqCqo/+Da7orJbHFDKNKtFKYpI3UCUa3mr2o2QcwpZjH00SVnCeO+T6I+GD3kSoV\n48cDStK4Vf3/47y0y3yc5rxvaFM3FdB5JyuEoCim/O/f9z38s3/2/Tz3wvmYrSUV4/GILMtZWlpm\nNBw2dUBQFjPyLK5qnHNopXA+RO62DySZIU2SeE9LQZqYSHPUmrzdhiqagve6Xda3tun0egxnFiGm\n1JVFKIWzDiVTEpNFwMnHaqC0jhSrhuI1m81Is4wrV6685OtzaztTIRqjiPguTHQcBy2eECRTB16m\nEAI6g07lSXs9pr5gqXeAuvJ4b/HWIb3hiM04fX2b7WARVuCDiz+juaAnTpxgb2eH4WhEURbQxKZI\nGTtSKSVBpg1ZvCbLo7bYBU/qFba2dFeW8dai65JDS336Jo6kQQuMiBw7az1GmwiWibjjmc0s0iq8\nE+SZZTINdFo1WhsSpUnzNtZaWq3W32rz9Xf1tHTCm770S/hXP/IjVDtb5MtLXN/cxDc+ssYkGG3Q\nRtPv9VhaWuTe+1/GQ69+JVqryFUUUBcVu1s7rK9vcOnCVWbTqBSbzaLTT5pBuyMZjQaUpcEYQ1nV\nKDmj0+kyGo0pZhOUUmxtbaO1ZjgcsrCwzGQ0JcsTkA4jPSYRTMua1QOCA8uBxf4iSbKATCRJuoAU\nGil986dquq04jiIjIOOdwAeNswJba+ra44Mj0ZbEBJS8fdH8eRHUQkb6100cU2DfNUo0nhlzFoVo\nepPYYoi4S/ae2XSKrUte9aoHeOzxJ1A6BlcqIuNlsLdHIOw7vpVlgUlMfEDWNc65/UKrE4P3gZ3d\nbV528g6Wel3Onz+PtTVKJQQnCdKTpCmplnhX0W8vc3oy5fCRVXq9RQaDEcPhHsHXFEWBFj6yb5RG\nSkG326W2lulsijLx57m/q4F6gWjcrKVCqdhRiqBwNo7aysf4Z2sdeWIY2wKkJ5NZvEETyayc0Gp1\nqGpLmgfuS9qc2d5kOCoorCUQ3WicczCbcKiVs5Iaro1H7A6GKCkwOmnUUJCqmHRZ2cZYWkha7S7T\neszqyjJlVSJ9zdpym5VOAkqiTMMyIGISSVNI55r/+R7J17GwF9NJHGfLDZTWCAm9tItqdxjsaJy/\n/TrT7soSm8NdqrKg1+ujEsM9J0/w0EMPcc8993Do8FGmRc36+jovvniKa9eu8d73vY8jhw9wzz13\noXV8A8+mM1588UWuXr3CuXPnqOuaNAsc6C3ivSB4iRIZUhZorZjNppTVjG47YzYt2N7ajgYW2qOU\nxjtHt9did28THyqMajGdjZhMDP2liqVuxokDiyy1W6RpG5X0kDogVYZWGkSN4oYOX6moAZdKRiqU\nk1gnqcpAVYB1EiE8UtcYDXl6ey5NhRSNSb5oUnabgtp0napRISFEVPSJBqD6JOvMJnpIRC7qgdVD\nBCf54GOP88pXPIiShtPnLkCQ+OBxro43UONlHIKn0+mws7OHANI83y9mIsBsMibLE+44uMxgbxdX\nVxw8eIIXTp0ly7JoqVeWbG/vkOc9pDBUdcXFi5ew7hydVptDK8v0O8t0WilHjx3l4p89RlUUGGNi\nwq2QdDp9RHD7O9qXOrf0SqubOGze2wYhjFSLmBIqqW00fS3qCqM1WqdUxYwgJEFKlMlxNpApg8fS\nRnL/0aMMguTsuYvUtcX5mn4rp9/KyQSEkJK2Evp5xtXtPay1MUBMCIKzDeFfkqV5HAmrGmcts9Eu\nB/vRTmyhZVDCIaSgngO20je+h9GlxlqHakaSyDsMjRt7wFlJXQeQJUJ47KSkHuyilbgtJYiLhw9y\nWAr+yf/6Heysr/PBRz/IxsZ13vl774yGEp0Fjhy9o4nyXYtqpU5Kp9MmMQnT6YSJt5w7fYqnn3qK\nwWAQFUda4cKQsppha9jbndBuTWl3Eqq6wPmouy7LgmJWMZnMSDOJMQmz6QClJYnS7O7u4kOgrEuy\ntE01A+ELtNrh+PFjtPIclWTIJEc3yLWQAakM1JKAR6nQjLI6RpZ4Q+1ybNBMQ41zAWFBK0GeKIyp\n0fr2HfNDuFE855zTOXd0npkm5//fHJhq9qThJqtLAfvmQf/4276D7/nub+fnf/GX2Nwa0m7l+ADT\n6RSpNUbHa2nrGqUMg70h1tbkeeSa2rre901N05TSOd73oadZ6KR0u0s8/ewpLIpgLfVszMLhO1no\nt6ikwgfL/fc/wOnTn0A3jvxZK+PshQvcdecap06fJUnSaIakYm6cQOBqFxs/Kcjz1ku+Xre2mAq5\nT/I1GHyDwgstIxVBz6VjAS+i0sTVRD6m9zgCWgqUiU/P4DN8x5ClGYPL1/j0e46Bj8YZXoZot9e4\n7idOsrzQpdXtcnlji1kRI6ZF8AilSJUm+GjhN5tM0Foy3J2wtrRGqjVSSYKK0QoiJJFIbKLZtJeq\nMVJpxh5iEJhzVbQDE6Lh6EmkAtsYTlsEtr4NKylw9txZ/vD3f48Xzp0iC5JzZ8+jld7vJHQS6S5J\nmmJry5vf/Pc4evwIq6urTCZjgi8Jvubs2bOcOXumaVACw+GQ/kLCaDgkzzscOLDYJMm6BuTzTRcj\nKMuKyXjK4sJhsjTBuh3anRZSgdEmxshYy2JvmXI4oJ06jh8JHFlrk7Ra6KQVb3ohMFpDs6IxKgEc\n8zgdqTQuAFIhpMYFgXUxo0xLyIwkNZIskUhxe475wd9QGzbT+/4mNITYsXpfR1WgmMdehn0mzY2k\njHlR9XzzN7+dr/3ar2Fzc5PxaILSimvr1+gtLtNqZTgfmQPaxTHf1lUTaBkoyxIBEaQUUDTdo7QV\nOskoXGDz8jpaaYSwIMDWlq3NbZYzxbAMnFw7yAcef4wkMTjryNIEnaUIJVBKsby6ygsXN6N7mIkA\ntUBQVrOoYGy8CV7q3OKdqQQpsNailaG20ZwkSDBaY13zZtYaGnTcGIULPpLhbUUWNNY7vFQ4lVKL\nwN7layy0WgQfv4/DkmpN7eKNIITEIqnrCiU9i92ogDJJSl0XDRwVaRox6yei/pWrKIqKXCtEHQgi\nbxZIHikiM8EojRMC2Tj7RyPrgLMeIaIIABHD1mjAFiECVtQIL4AExO2H5gdX8cB9x9BJxYWLF7HC\noU2LJNEsLC7w+je+kdc//BmkWUYIgaos2djYINWSdruFrWdUZXQ819pQVVUsssEznRZ0u12EkExn\nI7Ks1dyssVvI8gxbzhgOh7RabYajCVvbW/vGOUIF2p0OAOfPn6OddTHGstBXrCxnZLnZ54vOFT+B\nGAUeuMGr9ME2RsiRwBx89I9wHoIL8cFK3JOqhsyubtPUkrlrvfc+/s7B/zULPgHN38//xz43dY64\nR03NvJuNAPPS0hI/9zP/nt/47d/h937/XQwH23Q6XdK8G5kQUuKcI8tyZtMpJom4iZRx9SawtFpt\nlFbMbAxaTGSKSVJarTa7O5txqlGGPE1xPlAURdPEhEiNxCNEjCdPTEK71eP06fNxSjXJDQFQYxOo\nmgv5d7aYOqFQTeGcm5koqUDG3akPkGUpdVWTa43ynmll0anD20BwgpnJQSuubm6gxYQlk3Ok32NS\nl6R5ivOeTKfUtaWVZdja4RDoJCFLErwSdOSMQ/0elXWMJhO2dwcMqinS3hhVpEiwzvPsmUscP7JK\n99gqJupPmacyzu1JBGCda4QC8WZMjKGq62jgCmg9j8gVhBCpXXHFIbgNwXyK6Yg0TXntQ6/lNQ+9\njtbX9MmSFtY6Nq5f42PPfJzffdfvRLORwYROp8faHUusrR3g4qXzaCXY3rzO0888zZWrVyhnRbwR\nnKPXX2E2KwkhMB6P9tVNQjiUlhTllEQJilnB4mKPEALTSUmWpyAEo+GwifWd0e70mE2GLPQr7r9/\ngSP9lHa7g0q60HBOaWK6QwClFaEOBBFicq4nAipOxGgSJygrj6sDWlqEsCgdx3utFdrcnkYnYg4k\nMQedmqRR5qh+06veEN43K1Rx43MRObmh+TvVuKnJRKCV4Vve/g38w6/8Sn7oh3+EJz72JNvjCVoZ\n+osLCGEoZkXM2GqM1Msy5jghYDwZN/xSxcsffAXPP/8J6qrAJQZjIjMkMoSgspH25Jr7zVob13PW\nIiYRnHrq+VNYFwErQizato62mYmJMnetFLV/6UnjlhbTcW3pSwWNPZ6tA1LfpK1vXgCpZETQvSNL\nNbYIqDTD9TvUW+vISc2dSY5LFZkTTKUn8xLn66iccoHEGIQPpMZECzAhEVpR2BqpHUJJ8jRh7Evu\nSY/w7PoVpnMgKEBQNS7UWCu4vLHNq+5eixSY5oklZDR7qJwlUfFllUIgpGjiq7yeL8kAACAASURB\nVCO1QypFXVfcnJ0jpb4BUnmP1rcfN/EPfv93KGYT7jxxN9eurZO32hij6XQMa3es8JpXHWOhvwoo\nLl/apKo8S6vLXLt8mel0hAgF6+vX2NvbwxhNmvSYjsbYOqZEWmubblQSQvQ92Nnd5tChA0ijuXRl\nAx/A5JrLV06hE5hOprRyg/cwmdUUlWQ2rcn7ljuOtjm82qObtkl0O0aNK3WjM/WxgDZuwvsqO6US\nah/lotZBbQNVHcFFox1aWbLUkSSicS67Pcf8TzqRiI2g2f3PFYh8Mg2KuVjm5udH2GeTRTMiKZBC\nx/UMgV67zY/+y3/B7t4uP/Av/zXPfeIFdrauI2XC8urqvmOU9fHBGX92wCQp3jmMSbh86RLGGFqt\nNsA+mJn3uuwNBri6IksTnj19DryjrgVaSYSKSQ+jyZSgNVrGqPU01c19qJviLVBSMhyNaLf/ju5M\nCw/dqkbqBKUUQUcp57z4KCPwdu5SoymUweYJsiUYjIak411W8xTbyEy8FzjpkTagEhPNbAOERFJX\ndezYRSBJIh2qsiVKC1LRi3IzqcnzDgjH8cVVPnHtKkEKQl3jXUSZTJoRhKd0gdRopFb7hVA2ggPf\nKEGcc000h0NrqK2PNn4qRqQoE5kKN3iRcSenbsPZsJhOWFxY5OL5C+R5hvAOHKxf2yDLLZNRxeHD\nQ/r9JQ4f7JMmLa5sbLBxdYskUSwvdZHCc+DgEkUxxtWWLNMkvXaMmAmB6XRKkiTs7q3TbndI05Th\nYMpsWiK1pZX32d3do9ddwKSKS7tbJJOKYlZTVhWtLGOhEzhyKPDAvQdZ7BwgyVOSPMMYEYUYIqBk\naKhaTWSxrLGuRka7/ZjxFCR1UJQ2UFtPcA5pAsYIjA5oHaeM27eY3vh3z/08EdF+cj7Gz0d72Zi9\nuHCDPjUHrwI3xuI5qi+F/KQlbPCaxcVVfvzf/CjWWf7q8Q/yb3/8Z9lYv4oQkpWVFVKT4XygLCts\ns/JL0pThcMRgMIw2iHVNnmcsLi4ym83Y2NhgcXGJUTGjDpKFXo9Op8dkOiHNMpyzUd6tk6iMVCo6\nvJl27Eybfb+zFpMkdLtdrH3psfGWFtPlXhdfjdAhUNUNmTp4QohmBWVZ4ZTAo/GtnKGd0Z5UeALL\nUhMIVN7tqy+kjLusTDW6+KrCBY8UijRN9wm4ta9RIoJMdQgEGRAyEELNSqrZmdYs9zMOjjK2JhNq\nJVAhZk754AkivkWc9zhHsx+NN1R0ibqhBNEqx/kidthiXniJLllE1H8eHLjvy3obnmI2Ycc6ZqVF\nKUjTnNFogk5ShoOC6aAgN3uUkxlSXWV5dRXnZiwuthkNRzz1xNMgYTKdMJtOUY2zEzI6MnU6HabT\nKVprOu24Py3LKcFXHDp0hJ29a2xtbZMkKVmumEwKhIxglxQ1nZZkqTXi0x5c5chamyOH+rTaOdJ0\nGoWTQSIRyIasr/dZGLoh6zvvEUoQUNROUNSB0kb+caoDWSrRxqHTFKU0iagbAcntd7yPevqbwSXm\nBdRHkNY3Mmz/SfAUDb0JgogPpWhRKHDeNaIcv78h0Eo1ycE+CmGk5A2f+Qive93D/OWHHucnfvJn\n2N7epKoqut1Fer0uQcQMN+ccnXaHwXAYk4WVpq4tg8EQZx2rKwcpypLxeMIojNEmAwFH146CgPX1\n9bha0wpjElrtNuPRKOaCNfLyqqrQOgKXNwNqf9O5pbyNREr2xuP4sTH7+ucbqon4fBTtnJCl5LVF\nUeOJC36hRNPSy8apJgJBztkmTiFe/mjdFU9E1qO0VAqJErEbMVqiJWgXIzOMDhw9ssra4VXSZgwX\nQmCQKBdo6wSz7xzk9mle8+4SGh9IBN6JZuekIlcyCATRcUgrHZ/2c1/WfRT09jpVFRgPx8zGE2ZF\nxWg0oixLhoMho+GYqrAM94ZcuniRwd4Om5tX2dvbZjwaMBwMGOyNcNailKCqCmazCc5GffRgMGBz\ncxNrLUVR4L1kd3fQdA81ZTWjqspGeBGTLsuyxltHWQxIVMlCD1YWJQtdTaeVxPwtpWLn2XRLovlY\nzBVPKn7uiBQpIRXWeirnqS0xpqS2ECxSeJQCpUSTO3TDvPx2PELKT6ZFiebzEGL0z01ubfE/aL7U\nh30y/82703BT1wrN95sbpkix//3na4DE5HzGw6/m67/uq8jzmGw6mQzY29vBOkuapkgdZQFpmjbW\nnKEBqATeO6azKSYxHD58BKUUVVXgvePqtSvs7Oxw/Phx6rrenyKDD5Fb3BylVBQMNQ1SDOt7aeP2\nW0vanw7ZFYqD3lJqR+I1XkiCF5RK4nsZk/GIdjkldVMSKaiFJA9EOoMPFNZjtCQ1mrKKhSxLUhw1\ntpakWYq3JUbLCCLZgJYaK2KLL0TUGDdcYawypNphVIdW7rgj7XCov8S4GDEaFRw+tEKv3SbUzZju\nReSOuoBp9Nsirllwro7/zqThzzbdstIavCdTOZNihlAG50qUEtjaI83tx00cjka0jGY8iQ5cMdfH\nMp1NmE4nLCwssTcuSZKUq9e3yfZ20Wi2t7dwzpOmGbOpYDQZ0e/3sXXFlDEeRTEp91Feay0CQ7vV\nZzDcamJhStrtDlMfjXCqsqCYWdomJVUj1lY197zsAAt54NCBBTr9BZI8avCVTKNkVOp902fZyJwj\naK8aLlD0WfUIrJfUTuOsjLJkYUm0I00FWgWUCg1PWmCMudWX5lM6cdhr9sXyxn5/fnzjNxqaLnSf\nHD0HnW5qDuK3mlfbm4vqTfJTCYrGNrORpraSPl/x5i/nC970Jjaub/IDP/hDXLp0nY2rl+l0u/R6\nfWRi4qjuEwSCvJUzGg0RRMu+ySRGvrdbbXq9PoPLw2iU4hxVVZGmCQsLCxCgLssmE24+8kegar7G\nc3NQ+SXOLS2mVjhOrKwynE5YLSXjTDMNnvbSAnZnSGsWyFyCF+AaVHxnd5eV5ZX4i81pFD6amrRa\nCc5ayqpCBkGidOz+GqoNag4miAiqSxnjRPx8JNeooma532VjNOKAzqMGW0qWWxl+MYCRaCViIcWR\naoMPAesdaZbFC+AVxhhCiPzR+Q3lgbQVZaNKK8qijJScICJBWAq0FrjbsJlpt1NkCPv3lfeB8Xga\nvVvbbYbDEa3cMxwNkNoyHcXMdecVrVaHoihIWip6jLoo883aObOiYGl5hXJWAwEta2RSQhAxiXRa\nMJtWuGJGVQRMLyWIkjSH5daUu+5e4eTJPgt9Q2oM7XaHJOshkzbSGGSzK4vsgNjZBhf2gUXvQ/TB\nbTSSIUSj4aoKlCX4GrSo0drH94V0pCaJ4o9g/5ah8O/2iTWyMWT3n1wA9z9vLva+lwX8D3vSOP7P\nv1YKiX+JHXL0NY6vt1KNaEIIEIqu7tNudfmPv/Qf+KM/+hN+5/f+gAsXr7CzvYm1ntXVVVr9HuPR\niN3dHQSC5ZUVtrY2952miqLk0uVLZFnGHUePsre3x8bGBt1OJz4YmrH+4MFDjMdjptMptq4xSRJ/\nHxmbJsNLPxxvLTVKCZLKMum1uTCbkBpBHlLkcJsch0BRyf+HvTcPtiy7yjt/aw/nnDu8KV/my8qp\nsoasKo0lYUogUWBMIwNtSQwKQgEORwsi2u6g22EHRo3bHUQTHTR04wZjLDXgwCYQOBxgJGgmIyFk\nBEZSAVJpQiVVqSpLVTm/l/nmO5xz9tB/7H3vy5JUtBEZfpXS+SKq8mW+d+879+yz117rW99aK4n7\nC1G4GLnj+FrWbAq2MCjRxGiYTqe0bY0IWKOQkOqmEQU2heKSNaEheEIEbSw+NEmmFEKqtuoZTHBo\nK8QC2hBSgwUX0DbSs4nfQaeT2uVerMoapm2bNLHGprlOpgQUZPoixIALAVP0aPwYZQ26VVnTWBJC\nTVK43n7ezN7+CBUDxg4Q0ezs7BFjYGG4RGH7hODY299JB4ZXRC8oPM55NreuU1iLLicsLi6gNXjv\nuXZtB2Ms3rdEFdBWs7U7Qu8tUFWWyXSKsRVt69DGMxwq+tU+i4OWYd/yortXWVurWF0dsDDoEa1G\nm4qiHGBUhaQCcjw+FU/gMFKmopHoiT4nA31q6+gjTNtA4zSNg6b1WfamsCZgTRa4B5ekRbnL1O2J\nmxNH3OSZJuMZZ6Ecz22AMjekN3WXmoX4M80uWfT/uZRWJEuwchQXJM5fr5Ti0pXLvOENr+N1r/tv\n+djHPs4jf/rnvPM3fou93S2a1rOycpThwiLTyYTNzRsYbVheXmYymTCejFMepW54+vx5tFYMej2q\nXj+ViU4maK1Zv3aNsqpYWV7h2vpVtFbUdY02s6Tw86/n4VZA1Z7domVvb8yCtiy2LSFMcP4gPBr2\nbTolAgipMa9SMTVo9gEVIoGIIVIVPVrncNGjrKaJbW6tZbG6hBhwMTUhaX0geI/JWchI4m0JER8j\nR1eXcVs1C8bSVoJuHEpp2jzrpg0KYxRaZYE3uduUCD0CQVl8SKWFylgCGrzD6iQIHhQrTBjR1GMK\nY9ARJi6d5IW5/fyZ//7v/0888v4/4eMff5yq7OO9Y39/L3V82tzFhQl1nbLx/f6QtnEEHyjKgkDq\nh1mUmp2dLfb29tKYk96ArfVIlDodXhIJQVOETXwTGfQ8ZaWJBKzRLPTg6LLiyPIig75lbbViOKzo\nVSWl6ROLHkYPKOwQrU0qF1UpEzI3elmyljZ87gAlSaztnOC9pWkLJrWnaSNWebQEysJgdG7ZKC0g\nBAXxL2mM8ULG3LeMN/mZMQndc1VL5k7j54W+89r8rJ+SKM9505khBW7KjxwkX1PCSqEjeWab4snz\nT3HPPffOjfUrXvEgD778Jbzx2/8OP/22n+WRP/so29vXaVpHvz/k6OpRXNtyY2uHGEOKSKxlf38v\nedtA6wN7e3u5Z+kaGxsbVL0ewXtG4xHWWrTS9KqK/dE+3rnEkT8PDpWcGxWCLiwr/SFDIso3KEme\n3uwG13UN5AUgJoJfJ8K4dW4mAKQoiiTTUIqyLOYc28zNz4n+uewo5vEnM9nH7OsogsSIitBEjxiN\nbnziOfPrJSebTP46htTpBlGgTBKOqNR9RhubhrEVBUWvjylLbFWBSl3bbVmC0kTJBQuSpprebjh7\n593cd9+LaduW7e1t6rpBROVuP2kMt+SDaH9/jPMNZc8iEtFGsIVmd3ebuklTIkejMaPRBK0sRgF+\nQvQThqVmabHl6CqcPGG5Y81w9IhmZclwZEWzdrTP8qJlsa9ZXKyoqoLCVohYrOlR2D4iqTtZ+i9l\n8Gf6VSJz2meWvfYRYkwhZ4iS6vNjUmNoSW32khokdYxKmc9sGG7TQH+WxZ/PcSL3buXzvcnnhP3c\n5KneZChn/WBhJpvKOuwv5Oml0qp5qL9xfYP9vX0UKjed0YgoDAXHj57g+//xP+QrXvEyBv2SXs8y\nHu2xu7NNURSsrKywsLDIeDxlUtf0B0kNEGMe/Z55383NTXpVRVVVibLIlVIITMZjFvL4lKp6fg34\noXqm/VjCTo0rNdsSWVMV2jswMu8WNUvcOO9zaV6c122nTL5OhtH7HLb7+YKnDHx6jQ8R75NRTmGh\nJma6QGmVCfU03K8UTTNpCTGy20xYKmfGTXJZW1IPNG1LWRYYo2l8QBcFRa9CeY/PC47WiC0JKExV\n5JnhBjEW4z2xstTTCUoXiGrRCJ7bT2c6WCp55sp5pFhg4dhxzp49Rb/f4+ixFZ547OOJhxqNmE4b\nlpeO4NptxuMpK8tHkt7PewbDkt2dPY4fv4PRaMz21h4hXufsqZKlvqbfK+gXVRrxWwq2jEQ8WlID\n8KpSLAyGlKZCqcBg0EfbEmMXKYpFRCus6aFEo41K5aizkkfIhRR+bmeTSiMddCFEfNAEX+AaIASM\nBqMDpdEY4zBGISocGCIJPGce0m2EeZNnUXnkceJFZ8MeRR10VZsZyZnHqXLviRBTKSrE3Hxd5gZy\nlnpCDpqppIbPIVcUpr325JNPctddd7G2tkZqCB9QMUsMdWq4srJynP/zR3+U7e1NHn30UX7uX/8S\n+5MxFy5+lqWlVQaDAcO1YzRtze7uHiLCcDjIXGmi6kpjmOS+qdYYjDXE6YEdGY1GaKUoy+p579mh\nGtPCeYKFKIEz9z3A/hOPYYzFhBQeWRG8FlBqXidsjMZaoa4D1pZMJiOMMVS2xLWpZV/0mtJkTWgk\n9UsFYvSJl9FZ45YlU9ELpUnZwKmviUZjRVhdWuDS7harlNTMPJA09kSl+RWEIHPBvck0QVCafn8B\n5wOCRhmLFBatoTAarYSm8TQYJEzRikRPeJ+qTOzttwFLe4Q3vO67eOC+z/ChD3+IR97/n5nWU+p6\nypGVFbRJo5KNtuztTwFNiBXbuy2u9amZhYX7X/Ry3KTP4uIRegvC1tPXOXXyU/TLCxiWKcpADGk+\nk82z631oqPpJvmb0AKMXUVro9S26LBHVR6sCLW0aOyIALUonryl4P9f/pigFuEmY7kOaPOq8pmkV\nrolEH9AmYkwK8W2ZJpFGAt4FiAptMwd4G2M2rWKm455xmMDB2JIcKcLMK03c6qzLlBKZe3twQB1I\n9kBn7f1mHqvkw/HSpcvcc+7eeTQpRCSmRiep5WV6rTEa5zxHj67x2td+E1//9d/A7/3+e/ilX/53\njMc16+vbiGhWV4+yemSZybRJh1yMGG1R2lA3zfwwaJ1jMp1QzTzVENne3sZYM4+UvxAO1Zg2MSTp\nUtNS72yyqzWrTURXet41qnEN1tqcJFK41mELTVIXOcqiOBj9LMnYupgk8dElAxh8TO+n9bw+N0Ty\nBEOVBnX5nNSyeu6RVKI5vnqUyXiUOlV5T6FMPqFzzb42+JA4XBHBtS39wQJGl7T1lP7SgHFd09QT\nBv0+zjvGowZUKmcVFykLi/fNvCv7c6dC3h5o2jFve9tP89TjT+Cdn0tKTICmBmkCSlpEBeppoL+w\nwIteco6XvvQB+oMewQWuXF7nYx97lM8++Wm8r/HxGmFS8JYfeJBecRIJC5iigVDMNYAiQlRL2Vjr\nFK1ojTF9imKAkh5GV9lj8kniFJs8UTQ9/lrPkiGpiCJpg0NezwCSJHCpDh9cE7Fao4ynsFDY9DyK\nyl6WVkg0ODdNrfdvZ8TUBDvGiHNpvyEHGf2bJUSzn58Po5wp87OX+flvfTBHKsY4N2YiwtNPf5bT\np0/Pe1kAmeYzBO8wKjU8QtKKGQ4SWkorXv+6v8M3/K2v5xd/8Zf48KMf4fr1G1y5chlrS46uHk1F\nGUqlVn/W5JxJQT2Z5IGXKQm6u7uL0YZer0evrNjd233eW3XIY0sU06ahry2DZ66jzt2J/8zT0Et9\nLNu2RXRO7LR+fqOcS6eIECBqrLFMp9OcBXZoY/A+l75l2YO1lqausSpl1xt/8DDE4GiaZFiRkCQa\niaFmoTdgy4ww2f1XShG8zw1ZUvNbndUATVMzGAzwTphOW8qqz/50QlBC1RvgIgQMRd/S+lRlEeo0\niK9tWkIWBAd1+3U6mUy3cX6SSgp14iR9iJTlkJe9/OW89MUv4/7772dl5QjGWD7+2Cf5yEc/zK/9\n+q8zrSfgPLFtGE1rjIflxcg3fcur+JV//xGWVxZTS0RfoswURX9e3JH4uF5KhkAKz2Kc60aNzuG8\nkoPwUtssqifzfDO+T0H05CKoebiaWiRapsHQhkBjWsqixRpHr/RoE1G+hCAIjkgDscZIJOrbM5sf\nc7NzEUELKK0odZmMYzgwfN7ng3NWUupTAYvPM5lizkNApuZyv9/ZiJ8Qs/yKmecqbO9sc+bOM3N9\n5zzqJ42RlnxdRps8+WDWTyHM3iIV5Cws8I/+x39AjHD+s0/yI//HT3Dl2nWurV8BhKWlFfr9PjYb\nz9TXuMrqGp2y+FmFE1vPftvSq16gYb4TMCE9rDtFZGV1mafWLad8wCmhNUKFBhcwcRZ+CUEXWZ4B\nbtKC9xRK08w0ajHV87dtoPUpe+8lMMmdmVJ3KkH7NOEySU5zR3xRqYt4NpTtdEIxGCKj+qAhrlK4\n4FFKU+WWcqJ0minjn9v0uqwqMJoggkYnwxHToL3J/i4qeEKIWGsYj5MXUw5uvwTU5UtXmdaOgKKq\n+pw6eZpjR4/xqq96FV/zNV/NeDzlYx/7BB/5yEf59ONPcHXjKnU9oSgVMXi8a3DtOCXqQstrXvMK\nTp4Yggj9wQI6aggWbQ2EksjBfRYKZqOHrSnyBk4cHpI2dEqGpD8zjf5cb0lmeY9UoQZZYyopSRWj\nIgSFD6BVquPXAlrF1LNUWoTZdIXsJQVP+EukNC90CDLnSEMI88Ml3b/0uVLfjINEVczqmnlnfrmp\nI/9NCanPbWV3cwn277/nD3jjd3z7wZDLGNHKZmOaeqbGGCA8l5KRTAfGWYm4CC5HkWfP3sX/+k/f\nwo/8+L9gY+M6zjl2d7Zzm8U088lWVRr97VKEMjtoY+YkvHNMJi/UTvuzk8waagnsXLzAqbNnmTz1\nLCBoyDNdUh/Q1rnUWqtMddMhBMqioGnavBMEY1PHl7ZJIxAKm2bWA/R71fzmO+/QSvCSFnrm6SA6\nNSOJEXTExEC1sMxotE5P6TSWWVTqWxpTN5sYQYlBR4U2BQRBl4Y2Opqxo7c4pDQlbe0pizRypa6n\naBGCSprLyXg6l7BNxrdfP9Nnnt7k+7//n1H2Kna29nj66QtcvbrO//ubv8O//YWfI6LTwSUKoSGE\nmqKoqNtUB982LY1oVBzw4EvO8OpXfxWT+jxvfevPoOR9FLKQNrCeQLQpnMyheXCzDC94R2pqY6Bn\nCmL0cyXIbJCeFpM35k3Jypl3RCqgmNWQxwjeFzhncc6mzv7KUOqANZ7CRpRpQDwx6kzIpxrwqDUi\nt59mGGbypnTfgk/RV946+Ji010njHfPPJOdipidNBok8WULmXOhsRPPMCM9LAZRw6eJlzp8/z3d8\n+7fNFT2zBkJhtt7MrPnnUwcxu6UmD8O7WT1gTMk9953jF//Nz/DEE5/h9971+7zvff+Z6XiPre0b\nnDhxgtXV41y7tk5ZWlqX0sAzz1pEcCEk+eTz4HAH1DQpdAbBK2hubOCsZtS2LJQF2qUsfJR0A5VS\nWKOZ+nYuPWnaFqM107rGlEXSgUlqqpAa/pI7yiQhtQqpXlpLEmnPHhohhSYqC/tns9utKmj399lr\np/TsYq7pzyVmkpQBZTmTS+RQsrTUriUoodQGXGBvukNhLJHIZDLB1TU6OibT/fwgBlx+OLS+/RJQ\nyg74+Z/+WT57/imQgFIB7x1RR8qqnzZZFn9HrYkU+KjpVX3uOH4Hr37N1/KyV34F/f4RNi48zub1\nJxgsH+EH3/JP+cmffANG94nBESUdXjMPJDV0BqWZUzC2tIhOw+/SpFCVM8WgtQWEGEiNOJBcFpmm\n2Ya5VyrEmDh6H0pCKHC+BEllw9YIxqS2i1E8UVtEKgIGoQe6QHSFtYPDXJYvGrN7LJC76gdmDZ+N\nToY1eJdF9uRIMSeFUpydPf2D5tqzTP6Mikm/KN3vT37yk5w7d44TJ+6YJ7kOxqbc9PWsYCBDKZn3\nP7g52ZeSy3GerBKxcy/5vvvu49y5c/x3f+/v8SM/+mM8ef5pblxfx7sp9957jvX1TZyfMBgMMn3X\n0LbNPNH8fDjcTvs2fWBiZADEWFCsTxgNB5T7DmcDRpvcDaadh87SppNSEJRNQ7J6vYq6bihs+tkQ\nY2qGQhING6OJkdRdv9CENg2+894zaR1ap85SqdO2TYR7DCk8r1uWTt1JuHAFM6hQSlM7R1H0E4Xg\nIqbQVP2KJnhKSZINAkhZ4Zs0rmTaeprpJI+Yhkk9YaqbNEVgOmVvf4wCnr9j4gsX5z/zca7ceJZa\nxogEYnCpn6fu4STN81pcWmZpcZGXvvzlfPVXfzUvfvEDfOpTj/HshQs8+uhj/M7v/i772xsQRhgJ\n/MA/+QdMzWW0XsaobVoVcFPJSYs28du5EUnMdInWeRPl65rJ6FLbw5Rtj16Q3C4xEpOwfi6N1HMP\nJ/3d4IPBeUvwJUp7lLXoosTYgCoGoAXRFaJ7SeJmBogqEFOlGVK3IZR+bpu850CEmEt+k3RR470j\nxIPk6aw+f8ZvzkP8zK+SJWnBB9bX13ng/gfmSayDgqoZfZB0FckYRyDMDWqM2fO9iSa46UIz35om\nHsyaWkumK1ZWVviJf/7jbN64zo/9Xz/OU599mk984uOEkNr7Beez9NLT7w2o22ZeZvyFcMijEw9C\niNAAhaadjIlrS7SjFjEKFZPw21gz78hU9SrquqYoijS0ThuCz+NNIng/c8c1ICgNbdvmMSWpV6pS\nCtd6iqKgyd5mzCHIbPETwR2wRPo9y3qlWSG1DfMxcXY+t93TWqfO3UYzmU6BSFGUTCaTRNTH1LrM\nKGjrCa6ZwmRMNR6xNRmxcu8pylOLKG3YvbZzqKvyxWA03mB/ojDlKsPhgAcffBl33nmGr/m617C9\ntcPVq9cgCk3bcun8ed72U/+Svf0tfKizd5HGaiflqEUXBZOJ8PVf+1qCD0Sdkh0669qSWDyNtQkR\njClI3GYmP/GpCTEa78Aqi+RoI0iNaI3yw5SgJEJ0EAM+jJNkCkUMA4g9vF4kRAu6oFAxDXOrDLZQ\nmCJilELpRVTRR5Qlmh6iLVoZuA2VGcDcE5zpcGdZ99n34MDOhpByCVpm3eyZJ4FiriZDzUxiNo7Z\nY/zzD32IVzz44E39Y+WmrlMz3jLOPdtEvcTnXme+toPhnGGuJjgYNxJSL4i892ckefDC0bWj/MT/\n/eO40PJHf/wnvO3/+de0bc329g5KKYbDBYC5UuT5IPF27PfWoUOHDi8w3J7HZocOHTq8wNAZ0w4d\nOnS4BeiMaYcOHTrcAnTGtEOHDh1uATpj2qFDhw63AJ0x7dChQ4dbgM6YdujQocMtQGdMO3To0OEW\noDOmHTp06HAL0BnTDh06dLgF6Ixphw4dOtwCdMa0Q4cOHW4BOmPaoUOHjks3cgAAIABJREFUDrcA\nnTHt0KFDh1uAzph26NChwy1AZ0w7dOjQ4RagM6YdOnTocAvQGdMOHTp0uAXojGmHDh063AJ0xrRD\nhw4dbgE6Y9qhQ4cOtwCdMe3QoUOHW4DOmHbo0KHDLUBnTDt06NDhFqAzph06dOhwC9AZ0w4dOnS4\nBeiMaYcOHTrcAnTGtEOHDh1uATpj2qFDhw63AJ0x7dChQ4dbgM6YAu9+97v5tm/7Nr7lW76F7/7u\n7+aJJ5447Evq8EWiW8svPbzvfe/jgQce4OLFi4d9KX8pJMYYD/siDhOXL1/mjW98I+985zs5deoU\nb3/72/nt3/5t3vGOdxz2pXX4K6Jbyy89TCYT3vSmN7G+vs473/lOTp8+fdiX9Lz4svdMjTH85E/+\nJKdOnQLgNa95DU8//fQhX1WHLwbdWn7p4a1vfSvf+q3fymAwOOxL+f/Fl70xXVtb4+GHHwbAOcdv\n/MZv8I3f+I2HfFUdvhh0a/mlhccff5wPfOADfM/3fM9hX8p/Eb7sjekMb3/723n44Yf50Ic+xFve\n8pbDvpwOfw10a3n7I8bID//wD/NDP/RDWGsP+3L+i9AZ04w3v/nNPPLII7z5zW/mu77ru5hOp4d9\nSR2+SHRrefvjV3/1Vzl37hwPPfTQYV/KfzG+7I3pU089xQc+8AEARITXv/71jEajjmu7DdGt5ZcO\n3vve9/Le976Xhx9+mIcffpgrV67wnd/5nTzyyCOHfWnPiy97Y7q5uckP/uAPcu3aNQA+/OEP07Yt\nZ86cOeQr6/BXRbeWXzr4+Z//eT74wQ/y/ve/n/e///2cOHGCd7zjHbz61a8+7Et7XpjDvoDDxqte\n9Sq+7/u+j+/93u8lhEBRFPzUT/0Uw+HwsC+tw18R3Vp2OEx82etMO3To0OFW4Ms+zO/QoUOHW4HO\nmHbo0KHDLUBnTDt06NDhFqAzph06dOhwC3Co2fzKKB7+qq/g/gfOEZRgCoPRFtB479Fao6KAgCCE\nGBAjlLrEmIJ7H3gp3ljER7SBEByRQO08pVh+7H/7IcJ0RCQQA1hriQF6C0c4++J7OHPXK/j9P/gt\nHnrli7l85QoXnnkWExsUiph/b/4fqHSrlFaIKCKastfj6Noad5+7h96g4siRFbY2t3jmmWewGkQp\ntC3YWL/OpfNPsXNjEy1CZTRVVYJXjOqaECJegSkKlo+ssLp2jD/8T+8/rGX5oiAiz/k6xjj/U2uN\naEVRFDRNg7EGIoQQiDFijMEHD8j8NTEGRBTEyN13nuHlDz7AcGWZ5WMncS3U9ZSoFa5u+cgjf87l\nZy8wbWuOrKxw9do1iOBCg/ceAKXSe4solFJAuj6tND4qfAyokFZbBHx0QMToMn0WBIfLn1CnH4yg\nJaRrDoJWihDS562bGqM1McJkvPdfdzFuAZ749CdABMl7QCuV7k0EpRRek75P+nsIAaV1vgf5a61m\ntwmV77vzHgS01ukXSVqXUjRKpWfIaYVWGqM0Eu1znhOioVaOxk8ZSonWmhDC/LmZrTf5upyr5+8L\nMEu3h+CZ1iOatiWEiESNtRalFKP9XXa2txkOh9iyR6/Xw3uPCw2FtRw7euoL3rNDNaZK0kM+s1ci\nQgiREDxKhBhierIjBAKC4EMgqIC1FlHJsIn4tJnza0KIlP0S1zZo0mICWGvwLlL1+mhjiFFz+o4T\nFEWPva1tdAgQhQDpVTH/KaBCC6LAAxIRCVgp0eLRElG6oKyGFGWN0gWiIkprhitr3PPSv8Hoazwb\nl68wrCrqvX22N67yxOOfYDweQQwYESqlmG6uc2Xr+n/9xbiFmBnSGbz3iETatkWUpAc+pg3lQyBG\nkGjSJg0NkbRxBEFpRaRme/MiLkwZlIZicZUQQNsesbC8+usf5rFPPsbu1Q2euXiBwhY0bZN+udx8\nXfmZE8FH8CESJOK9S5tbC2727KHR2tDmvwNolTZ2Mrpp8walidmAtjEQJYL3GG0IWpCbL+A2gg8B\nEdBKg0QiEImIElDZGArJscjGbGa0VDaiAC5ElFY4iSgFog2ESIgBRKElIipSN4LWGmsN4iKoCAaM\n1czMVAgBp1rqZspk2iLiWRgO0VqntQhh/tz44FFKYW01fx7Tc2iIsUUkG/wQEAJEdeAEqJnhd5h4\n04HftM8x1p+LQzWmMUQKkWTYApRUBCKeAJJPeh+IRJTSKKVog0uni4DSGh9h5tEYrZn6FgGssRBj\nOllvMspKK8QYjDHUdc1dd99JcC37e/vJYwkhe57zq4QYidET8iOVTmqDg7wpAz7UjPyYRhxKHxh1\ngqJuhJIarRzKRpZPHOXUvXezevYUk/EOvq3Z3brO+pVrbG1tMRmND2E1bi1mD3DaZIooEGJEZSOk\nlKJtW5TWNK3H2IpWALHJW1EKq4VJPeXC5XW0sRxthH50HI2e3mAJ7yeUpkdTWR561avYv7bBpK3Z\n2dpGTYXxpMXfZNi1Vtlgp2cKUYiAyd6N82kzhhgOPB5AKU2MAR8CKhuNENLPRAKoHDXB3Nt1zs03\n9e0InQ2KKJl7I6JVukcCMUeOPqQ/w8zIZG8VScZPIZjsreoIMd9D0SadbFHwTnF98wabW1sgwtra\nGneduiN5osCMjYwx4vaewrerbFy8zn5RUd7bT05SXVPYZKi5afeKyHO8V6U0zrVze2K0ZtK0KFHZ\nmQsEH0ALbXBo39CXPsYYnDeE6Hg+HLpo31pL2zpQyYd0zqGMhgjezx5QhcoftAyCInkZzvn5CZm8\niRRUiCjGoxFaCaE9uLEhRqqyR7/fR2nN3t4uR1YK2nzzUyioCDE/EDG9FxJJCypEpebehvc+hRYh\npO/nsDQGn7wrIkVsiaMRtVZICOBbvCoIYoltRDtQGJYWjrIwOIYgyG3IZM9O9ZsR82EWgkeUTn9q\ngzYG51psYbFFH1OWnL3/ftw0MFgo2b6xgYqBG9eu4H2L855nnr3C1o0t+vostjAsak2/OkL0NQvD\nJRYWlvjUpYt85+tfz3v+4I/Y2LqBKMVof5+YPcYYI96l58laiwseET0/rGceTmEKHMl7VqJpmiZR\nTtnzqnXEiOCcA6uJIWDiwSHhg0MbjXceq2/DxSSZoxgjEtNnlnSqEGM6PGII8wMlBJ/2DgIxEjK1\no/Jnj+HguVCiMm0HkYCI4bG/+AyeSNmvaNuWy5c3OHPqFNoHiA2iDTEKIutU+8+wOYIjpxcZhrU5\nJTS7ptnBqZSaH+YzmkDkIFKI8SASVkphjZ0b7/FYQaZsfIg45ymK9Fr1l2zOQzWmIoLzLp30pJBC\nxKeTIfOkotTchddGE1yLtZaqqtICRpIRjZGZyyoiXLp8iRhDerhjhOhTGFBU9Po9YtRcuXKRY0fP\nEo3GGo3EQPQHYR2S+dqYDHV6EBSikoer8ukdRSHKoJShLHpobXFuClERIxRlSes94n0+mTWudbTN\nhBBaXAh4EbQGjBBv4nhuN3yuUVVKcgit57yZMYbFxUXK3oAHv+qrOH7yNLrXxzSOjavPcvXZp6gn\nk7lXEUmGcNI0bO2NGQ56UN7ALhuCB6stFzc2iG3N1YvP8rWvfoiVY8f5j+95N/v7+2xubbK7u51C\ncVLUYKxldfkI4/EY5z1lUdI6hw+eXlVR1yNUAO8cImlzzj6XMQoaT1FYau8xWoMPc840Rgg+bezg\nw2EtxV8PEnExYFWOLIhE32KQ5BTMOMpsUMlrNAuTI/HAiGb7E2OOBqLCR4UKghLHAy+6l0c/8ili\nPpB6vR6jcYNZ6AFCcIrJ3iUq9zi/9r7rHF/Y5sxDX8lKPzIZj/nEU0/xspe+LHmms8vPhvTmvx98\nrVBKaLOTOXvGIM7XL0aNNoamCfjK45xLtM8LNcyHmAhpsmeZFyIEj0JlYyb5FIv5YU4/kxZQ8k04\neL8E4fr6ejaucf7PMUBRFBhj0LrHxvVP49rTuLpmFtiHmzguUQeJlGRVk+GEwM1OWHoI0u9VohKV\n4Mmnd8QYS8hemsSYwpx89YkPniU+hBAj8Tbdf5+LlOgJGKMJojE6hVZFUWCN4cjqMY6dPMmRY8dw\nvuHys+e5fPkS9XScvFgkcWBKaNuW1gV29/YYLfYw4x6TYjdzrQWT0YTR/j4SInVd0/jAix54EdfW\nrxJjIATHdDrFWIuIsLZ2B00bWD06mHso3icPa3dvnyMrK7hpw2Q6hfz92eHsJGKLAt8ccMAGhZ85\nASo9B8lRuD09U8mOSkomqWQYI7jgMcw4U5k7OjNPcBYlHrxR3lMiaAxgE6+cjZeIUJYGpdLe1Fpz\n5fJl7jxzjMGgJCjNxmef4Rd/6d/wj7/7fp7cOIKOV7lTIOUxC175yleiRdOyh3GKuuxRfQGuOtER\nBxeWjDs5IZa/IObDU2OMoWnaRPtYjfMeqcvnvWeHa0wV4B3Rg7Ya7x2BgyxwSkJlyyLpAQ2SNli/\n30//ng679GXmrZUIj33iL9AkTma2aCFGev0+1hYMhkPOnDmNUsLG+jVC2yZOBo3Op9rMqEWR+cM1\n2yxRZuFCCgN8jDgfKIqKsqxopiMigg8RYwoUEasNWmnKskKCIobMwwooBNEKbdRzDPXtggNvNNLv\nV4l7DB5baLxTeBxlaZPXhqIG7j53D0oi169dYnfjGo9/9CPc2LxO3dSEANYWc0/WCkzryI3NEW1z\ngbM+0o4nDIZLuCh4lzh0ZSwrK0c4MhiysiIoHdmra67v7nHizAnWTp6gbVuOHTuWoiGt0MagtWJ/\nZxfvUgSzfm0TWVYYCzs7O2it0cqys7uD9y1N21KK0DiHn9TEtk1Jp0wp+BhR1nwe9XG7INwUMs8i\nRSBnvJPHLSpl7JXWSFAUIjQxEJVJP56zvyIhqQCCx9HgMxUXQqRUliZuU1Sewhb44Fk5coTlpR6e\nwNZ+zb/84R/lgttlb/PlHFnqsXziAVRbsu9aJutXWOovUx5dxDSGvek+ywuWUNnPU5WEmS1hxqGn\n9QpOEGWSkdeCGI0KAStCVBDQuGApe4v4uPW89+xwE1ARlpYWCSFdSGEF1+ZvZgNpkPmpF0PK6Btt\nEk85u1nz90uhhRC5cvky+JASBMKcLynKKmXZh0MWFxdp6oYrV66m15LVBfFzrzPmh0KSBxs5kIUg\niAtIiOByhjBGcBG0IkaPa2oUgUG/h7KWwha4JnuqIqjkhycvRlROdN1emD2wxhh6vSpz2ulzNE1D\n2S9oXYug8A6Ora5w5eKzPPaJjxF8YH9vD+8mEHKmX0BFz7DfJwZH0zZoCzv7I8ZTRa/Yot5vWFgY\ncfykZTrxWF2mdWpq2npMNVxmMBxy4uRJzpy9k8XFZfamY86cPkPrHYPBAKU1JnPdzjlEhKZpqMoB\nMUam9QjvHBHQuqKpGyaTEdevX2dre5vLV66ws7lNaGv2s2cc3STRS94/J7y8naC1Tny3pDwRUSEo\nqlZTW6iUwilBa5NVN5GpRCJCiQcLBAuiaEOT5FNREb3C2D5KhGlTc33nBvv1Na5v7bK3HTh5x3Fe\n9JKXcTW2rD895dr2BY62wqfriOyVyKf+kIsf2uTqjU0YjXmwWGJjsUcZBepNdoZrPPQv/hVeBaqQ\n400RlGRuVTwqhpywLonUON9g+xZRy0wmLU3bMAmR8bjGeY+pHUEEtz3By5C1lS98zw6dM414lCZv\nGAhBzTOqkqVOB1xMypQqhNIWaAQfXTJF0UP0iCgKZZjsj4jJlKYPqjUxKkxZgij29vezrCIyGY9S\n4ij/vpnMambAQ+Y5Z+HL3NTFZGhDDISscZs2Lb5taVuXpTgB17SYQmgkYIjgBN/WeAWi9NybnqkI\nbk9vJlKUikG/ZDKZUNcNSiUpW1JWkBM0oPGMd7f5zLWrWGPQWjPa38FaIQZFIRqRiDGK6WgvJQKV\noDQcHQ6oxXNj8xr1eIBrFlla2kZXS4x39jAYiiMrjJzDBEW/qrjz5CqgGCwscXb5NFU5QGUpjDY6\nRR6iIcakxIiZ9wwBpVYy9+nn3KFznnPu7qQ9bAM3dra5fPFZrl+7xsULF7m2vk5sHPv1iCLensa0\ncQ4bLBWKpheSQSWwVQaGUdFfXmPYX8S5wKRpMEVBayLjiUZNR1y7OObaxjOce+AEtRie+PTjvOjc\nfRw7doTre9scHx7DKMNwoaSv7ubfv+/dvOG1dzEcK/7wz/6U8+yw1giP/+G7WHj2k7y26vHo2/4d\nr/ANTheAwolmvW1YvzZiJwRe+8/+F1527znswGBCIErOHQMRDWKIQNM6QohMpi37dR/vF7i26emV\nU0RpXBykz2QrfHCgoG4j1cASJg3Q+4L37JB1poK1hhh9Hk2Q+NCZ3EIic8OaouGUkJhOa+ZkB8xl\nGCEEvA9oU+J9ugkhCCoKhSlosCnJhbC/s8vS4oB+qQnZIxESKxDTTponoMiZW10UKYwDhCRATpxK\nzMafzMfquTft2hYIaF3kTHJEKcG1DcborLHMXnWU/GsPXWTxV4YI9PsDnK9RSlOWJc61EKHX62Wv\nX+G9oywrxvt7BOeJocjJHY8PQDTEzJUCVFUFMTKejIkx0i8HTOp9ohi298eI1pxxEdmfMF7fYOI8\nPniWjx/HVjugAtomIx/ciL31McM770IVGqvTsyBGoVQxz/xC4sxmgvOkrsueTU5wep/ogBgCg+UB\np06foJ1OmeyPefbCJd7zrvcw3Zyiw+1pTJUT9ottQhgS0ahqASkGeJZomLC+fo2Nsub69g3uOHEc\nEz3T6zWf/vijHD16lAde/BLuvv+VKAXBR04//HUoBePRiJPDkjpuUpgK3DXe+bPv4hu+8l4+8+gf\ncXUr8lt//F7s5Wc5PRWOHF1gR3psbo24tnmD3WnLqlX0jiyzFiqWqgmr1RJ3rS1z9qtfibhAbBsQ\nwQeFCwHnPK0kjbkowflIUQhtLPB6DMZRmFRYEkIEZZCoKEyJF009HdE0Nf2FHqZ4fkfnkD3TTNbP\ndHpz2UEK7WeZUJlpKfKjXRQFKmvHYpzpQGceXTzwgjjQbCulsMrOKy92dndYXhoQQs6wcyC5Em4S\nW0cOyiZuwoy7BvBZg5j0p8mznlMTEUTl5FaYZRVlXpgQZrq82fV/ThbydoEItE2DMuTMp06hlDGZ\nC59p/g70fLNQsm2bvI7p5900ebVaKZqmyaqJpA+MRKwtaZ1HG03tPM55YmiY1GMGx9Yoy5K2bZlO\nxgTxFKVlodfDuZoYDGVhCUpyokFQWuayOK2T1EnptDViruaZGc4IyVPWGowheIegqesGKUu0NpxG\nc9fddzOa7PH86YoXNsqBRkfDTrnKxh5srDum7Q479Q1efvcCXzNY5hPPfJbrW+v88bt+k9e96e/y\nzGevc+bOszTTKVWpUMplaVwENCIaUQqk4PLVG1z8zKOsmoaTxwquPPE4j33gUXbGE+SJa1y4/BnM\nkZNsiueo7bO4sMSwv8QbtgourUbWpM+lBc1KPWWhjexZSQoBgZ1xnXj0mLxTW1ZoMQieybih3++h\nNQyHhnYPEMW0HmGMQmthvL+f7EJOmsz2bIxJp/p8OFRjqrEMTEUUg68bCpOqUBCFjrPQIidolKKe\n1vR7JcOFYUriaJ1LSHP5Icmj8M5hjaaZBhQanEdFoer1M2lu2NnZwZ88TptFxDEEAgfayJh/NRyE\n3SEEghIIiZZQkML1tsbX0yRczlnAuUEUQAIhCjEqlBYETdtO59n+WUiZnJgDCc7tBGNMWj8Rmlgz\nHk8REYpekQT7IaSoQCka73CNoyYkz1OgbQJtA0pNMMbSNi1FSB6+1Sm0Rhn2nUNE4V3LtG1Bw/bO\nlNXhIsVgyJ0nTzH1QlEWbFy/xvnzT3HPvefYu3GDojAs3nkfhS3TptZZFhNkHhkpZdLBHHL2V55b\n0TXjQQ8SNCXeOQpb0LYtxEjZH/C6N7yBlz74St7zrt891HX5YuH6x+B6wdPnL/LEpSv0JbB3Y4OT\nq6s88ckbbB3rsbE5pXbC4soJfNCohaOMp3s89IpXIBLweJomUE8bRAJVVWKM5VOXn+X97/4gg7jD\nX1z6DJ/+4GPsTBrUjW0W+wsMbQ+1sMjr7z3HiilotPD4Qs3pusexf/i3Gf/enzDdaHjV2bN89qOP\nsFMOmQ56jC9othYcxvRQAgOrQNKaOD/CRwVl8lATn+8QIk1TUxQlSnSiErWeFyKEGDCmIITAZDxi\nMHz+kdOHakyHKtCEiPItYgpaQEJIvJrkkrPoscbifYPWAe+SF6mjpKSPSqdRJFUitW1gf2eHpqkT\nxxkiWim8S56gsRZjDb1ej+AdtW/mAqtZKivclNS6Kbs159Jgxvem30nwxLYlek8ohaK3ALKO9w4V\nI0xaKJIQWLRBaUPT1LTOZ31sFkarzJmG28+Y9vsDrJllTScolbw+a5MYejppsUWfqlexvbWBSNIV\nW2Npmgbv47yyqG1TDfQsedC6FhFF2zZYKYEwf9/RtOXC5Qs0K0m3urG9ASh2Lu9y4cpFhgsLjMc7\nTPY9K8fWuOfo0eTp6iRA1whoc1OlTMTaAu9SNZSLN8l+4Dlfz0oLtVJISFSVcw5lPZWx9JeGrBw7\ncijr8dfFlT/6LVaHA84dOcHj6xexKyc4unyc0bTGVxVX9qCoDcuLFdsbF/m1X/h5/od/9AOcGByB\ndo+RU+DS/rC9Hn6q2R5f5z/95n/gyfd9lE9/7BPgPP2qYln6rBUFZc9ihz2GYhiXJ9G6xSvD3nDA\noN7n5P/8g2z+zL/l+tV1jv3db+ba73wYJRW9WHPv4Ax7gxEGg1WR4CORFmOhbSYUeoF6XBO0pRaH\na6doW9K4NkVUVY+mqdEmSRCKIiXPQusgBpq6RQSqsv+89+xQjek3fuX99KKDdopHI0VJDKnBBMwS\nAKnyxPu0+WI8kGy41qFMzqiHiBKoVOTZi1dRxhG8JUZHScS3jqroUdc1Yit6vYrWtVy/euWAG5OU\n4IpwE4UA5JAz/WAKxWdFAj4EnPe0zhNj2liDhSHGWhyRoqwoBwNikcbVziqqvE+cjsSWgEJpk3oN\nxIitvjDB/UKGNgExBaP9OvOOMg+Z27ah3x8wXFliUPYIIbK/c4NysITGoaYTJFcohSBYq2hdi48t\n1hb0dJG8hJA0i6lZSpmWwqV/j8rg25roW3b3d5lOpwyrgoVexe7mDYZLq1ze2OIlbZsOQCVJApd0\naUnig1DkscK6TM9aqcyBtAbJdecHnqkizrWYIQSs1olfLSO6dtx99vThLswXifvvfxU3JjX/4bf/\nI6dP3EUv7ILSRBVg8zrPPvkky2dP4LYVEmp6kynt3iYju8qsKcw+kQXl+J1f/kU+9icf4MpHPoWO\nBdoWHDN9jBUWrGFNVZRWo/oeKYccRdPEkrhXU6/2GI6m9KoV1v/5v6L5G6fp3b3Iyq98gN2hpS4V\nWlueuneFlZ5iTQpcnXIuTQ2uFcpyOU2ojSYV4KgIYpIMzocszq+pegNc3VLXU8bjPYpygGsbrLXz\n/g3j0QhY+oL37HClUeMRpd0k1PuowTFkKdXrHgidZ6VsMufNJDegCCEiihxSH4joJfb5i8c+nRqS\neKj6A/7mQ6/g/MUNvF0GgbIs0GNNaCPT3SkBSxSHUZIE3rMyyJsVBUolD1IOrknUQblcyugn7lQX\nlqJX0riGqBTeaExp0UZTGptlImFescU8vFAoUelUvM1QFCU+RFrnMjcc87mTmoO8+BWvRBWWZjKl\ncQ3jyS7H7jjO1o1r9IZDWh9o2xatc1itoG0DZanYbyaURZnlVolPda7N5cSW61u7VGXBkeUB17c3\nUVphyoICSdpTLeyOxjgvfOhP/4y//S3fjB72sUWR6u6VmsvUQozzkkGVi0NmDTT8TVHJzJMlJ0tn\nPQBCCHMNsjYuJeFuQ/zEL/8yoW2I0XDum1/Exvkn0VG42mzxqYvnuada4MW7Baf6A0o75kWbA37m\nrb/Aa//+mzh750m2LjzNT/+T/53L55+mlAKtDdb2WEJYMYayKilEWFRJmbOEsKcsPaswonBO2Nje\n5a6lRYJSXNnb4esefAlbf/oUG3fewcdOKO54YoM7JLK5oBm+7Bwn9jVTNQFlESLjyYSAZxAWU1I6\npD2utUaLZTIeYW3BdDrB2gLXNngfMMZSFBXeN7TtBFGetmkJO5ssLT9/pHGoxvSp7SmLS0vQtAQ1\nIS4u4XzIxnQmQ4opQ681SjTeJwM6y7I+J/EEaNGMJiMWBwNWBovccccRCmoe/qqv4CPnr6WiAK1T\nWZtSeFFUg0Wa6TirCCL4lphbrgCp8onkuQRiTogd1Pj64HE+pHJEn6RaM4lT7Vpq32KYbc5UATRr\nmAEKLSpvYJnLsm43SOYWtdY0MZVcikAMwqC/wLG14wQt7Gxtoa3FlgWmsPT6fYJM5ln0FO7nBhk2\nd+4xhta1aGNSZZHzKcIQRVPXWB2p2wYfe9STMf3hMCUQSJ2qEM3+pGFpaZWqLBN3GxOdw03i9KST\nTWHejMaBOE+mST6x07olrzbeFEUlzl7NX1cUhhCevzHGCxkrx09z/eJ5CmsZb27yoUf+lHZ3D7VW\n8qIbLd9x3wOsacONdsrTI0d73538ze/9NtbWr/An7/wNfuVnf46tp5/lyMoaSlsKU7CgPEva0LcW\nazSlUlQ5aSvZOUp3L1BoRTOpCQKTQrGvhemJRaafaHHbNYtPX2X72CJmPKWWyNqxFdqeZd832FFD\nUaSKpeHCAnU9xYYi24qWENP6zVoHBu/xyuVCIcNkMmI6GWPLzPdHCFITxLCzuwN8YYN6qMb04uY+\nD77yZegmMJyO4NknMCjM8ROExaMYrzE+aTMbFdCVodAm9SWNcd47MUaXOgEFT6Ei3/CNryHcOMFj\nH/ww9aXPsqEc9e4Or/9vXs8HnlhncXmVrd2GV37lQ3zT69+ENRWT/ZrxeMzvvfvXuXbpKWK9i2un\nNN7hfSCGVNUS9UwxNeNXkzjd1452v6a2PZQERDS2KBk3DVPnWC6DF49fAAAgAElEQVQKhv0epS1T\nsqYsqMeSy1OzEc2crJ9Xatw+aKKm8Y5mMgatCKkMgagCCyfvQInmzJmT1MeP0Ss08exdBK1ZXV3h\n0sWL9CZT9nd2kHxwSvbenYs4arRKEjZIeuEDUXzAebi+tYu1FmsLJlv7uVtTRKSmqhbYH09YXA4M\nl5cYTycUVZG4aQ3mJt49hqxEUEl3KiIEAkoUVudDXDFvvKONIcRIoVL5c0pkCs43BO8xt2mjE/GO\nZq/mxnSL9//hH9BOJtQLhqWdhrXhMu+L+/yt73gjR8/dx2oYc/6JT/FT3/BaLj11kdUI9SCyhGIo\nkYGK9DX0ih4LSrFoC0pj0UARM+8skRAcRoMKAYmBzXaMJ7I4cjxEj49/9HG21wz37E0YDs9Q+TE7\nLnLEBEq1iNkeUVqFLQ1KawZWY6zJUWaKOEb7uwwXhozHE8qywhjNtJmifWp3pJUhEKjbCaawWGNQ\nSohe0U5qhoMXKGcK4KoewQh+2Kfc2cECW1cuUdZ7qKU12kGfmkg/WKpWkCo1oBWVepwWStG0jtSt\nFlzcR8cpFy5cIEpFUZVMxlv/H3VvGqtpmpf3/e7t2d71rLVvXdXLTDfTDMOYzOBhibGxDDKQgDEY\nA5awE0wiRbasfLPkbLIcyQp2bMcisoWJgMHIBBOyjAnDFjJ49umZnp5eqrq76lSd/bz7s91LPtzP\nOdXg6ZEyg1xd95dudZ86dc5zv8+9/P/X9bsYZ+sczwU/+qP/BQ6FDx4ZLG13JbXjqGX9yaee4d/8\n+i/zb3/vIwi5QNr2IVxWCFrvz67op3ZT311v44IeLSNaGSQ66kpDBbbC9AYUgzUGacYwywi5YVnW\nBNG587vSwWAweCRz8bUM7+PJPMrc4u+hpCTrj9i+cJG17U2CdeQm4cqN6/imJQTJcnpMU1U0yyWr\n+Twumt7jrAUpMEkkiJ16wZ2Pf5fzD31izgXmZUO5s49SEi8lRZaTZzoqO5YtLnjuvPk67//gBzHa\ndI638JZLgDi7vgeIDIXulPnwtHnq9HqI24NuYe0W3rgJdF/rPLZu/j3NwB/vuLC9yWzviO/98R/h\n937rN9hrpqyHjGc+8PX8yA//ECrLOX79VT718/8LP/1f/Xf46RFNCLShxaYpF4JgqHJ6jWOcGaSS\n9JQhN5pMSxIpowbVO1RwcesVEu+ihz8Ez6AYo71gaD13bqxhjOKJL+5TjxruvfcSaweBGy83NEmJ\nac5zlO+RNpsE3eCcIMkymrqOmxwOayukCZT1Aqkki+WUtm1xjUclGinA2xABN3XJ8XLSyfY8AseV\nq9dYLe4BX74O/kgX09xoEiFoEZQ6YXXxPLJt6U8Nw1mJOHyT9tIW1aCgTgYYBD2ToJVGyI5l6i0y\neKSPV67pZJ/yeEKajghFS2Ikaxs5m9tbbF99iv3jE0JwXLpwHZNEi6NzFU276o78CX/2u3+QP/Nd\n/zE/8w/+G1749MdQCtLMkGY5iU4QgrPT8XBjHWVMx7yML6j3gX6vH5smUkeDndVoqQkuIETg4qVz\nvO+9TzNZLLCt58HBAQ/29hkOCp586sajnJavaijnSbOCalmhAtFWiadIC9YGI7JEo3TcBAemoE0t\nwnmcLbhy4zpVVbGYHLFaLqP2mLjJed+SmgJrW5RK0CKmKjQh0HQ8hdBVhWxweBdIte4UadG62oYa\nIwwaw7A3RBtNEB4tJNGOHdAqLoguOGTnuAp0zjc0BEXQ0ap8Vlt9i+hCifj5CwQMceGvfUtrF49m\nQr7G8anPfYY//13fj6sdFy8/wV/+nh/hxtXruB7s7tzlP//+72N5dxfloRbQ1zD2CisTaltzLh+T\nJCljY0jxqERRGI2REiN17BMAKngIvoPRRCtxqwPae9YHPVrXsi8DvftHvLk64eDKJW6Mt7nw4qvc\n+BOK199ouFinrFzLnjQ8a0sqkSCkoi4bkiQHIVkt5iRZQds2EAJtsDR1jbMNy+kRglM7MSSJJk0b\nlPTkWYoSnqYNLBdzTJK87TN7pIupTg2+9ihtkCGgXYoNknK8gV1zmMMHpA8eYO5DcmkLtXkZfEAL\nSRCxG7taLVA6Ak3acsnJ7gGTyZS2tTStJdhAf22EN2s0zvGL//N/z3Q65e/81z/NZu8COhFRcyY9\nPjhaW+MsaJnyn/3N/5bPffL3+Wf/9O/imymT5Soujh35qD/o07Ytg9GYJMsxuiNSCUiHAwiBZVOj\ndZT5jNfW6Kcw6Oc0jeLjn/gDsrRAKo1JM27cuEavyM8aW4/TqOuGRkmU0ti2fmhc6ORO1jqyLAER\nFySjNTqJXNHFYs7GxgY7WkfClu80xt1J0do2dsqNiCUX4kk162IwvI824lOeqOgWtqZpAEFVVZhc\n0+v1OD7Y4/r1iyglzhpMqvtZTy2jqsPtPayldjVh76O8LSrRH5LOzuqrcQQcztc0qyXyK8CE38nj\nT/3p72HWtFTVih/+iz+E1YE9Fvydv/zXeO13P0ZaLRmRIIXGIMiFRGcKJRIqn5D0+qyrhMxEk0Qi\nJaZD/npnMTqCg5QEbyN138hI3k+8pw4WPUw5DjUUCbtpRbVp2FyUvJLtM760xht3VqxvtvTqjHq4\nYGw998p9Mjbw3pFmCa6qEBJsXeJqR1kuEKJT4rSxfDTspbStRReRyu/wlKUleEvwLW0IpFkRy0rt\nOxQOHWUoMULCCEWIzVGENNRSo8cbZGFOUrUsH+zB6DymO3WcquoPD/bZWF/DWomtKubTWdeYioXs\npm04ni4Yiozf+d3f5P692yAEX/z8J/nQt/65sy5u2zQ4F62fEkVwHpnkvP+D38b/+7v/Jy995rfJ\nhUfIgA8W31iWs4ayrhBKM15XZyoEKQVadszVTJJlml4/J00UYBkMeng/5sKFcwgiWUjnPYregMFo\ngE6yRzMhX8OoqhJRFGfE9dM8nl5RcJqCYJ3D6AgTVkpFx5mMrpK18RilTj+OEZghuyaBEuos4kR1\n3wdi7AghRLK9c52jSUZDhxBEqprENzVt23J0dEDTNFEF4M0Zjza6QgQixEaggAjH6OZTiAC4rrYq\n8MER8YkPIeJ0m7v3sUXpm8iKeEz7T7StZWf3Hj/2Y3+JUJa8+ulP8H99+MO8+lu/R64kIk1IrUIJ\nUMZjUOQqQZuE1CdIBIU2aCEwQqBjeZrT/qrwLuqqvQcCtrODnzoaCYFF4tnvCZaqxqQJK+HI+4FV\nL+BpqXSgSAz7ztCrBKJSJNkWvX7K5OSA1WpFpqI+2dqGgEVISV03nU45QUpBURQcHh3SNh5rW9qu\njJMXPdqmjohFY6iXq7d8Rv/d8Wi9+VLQtC1K6bMHrDvBvkDi0nUm5/oUMpBOTkhf/DTzNcP25e+n\nlVv4peXOl16ieO4ZZkuo5idUqxItBa2zHJ7sIUPgyW98htceHPAHn/gdUtUyyBI+8xs/z/u+4ZtJ\nEkPVlDTVKlKpOpiFVgmWeHL6L//23+Wv/uj3cLR3G60blIjZU6m3iLbk6I0li8O7CDdnWzyJNIbe\nYEgtBDpYekXCcFiQJglZUpCmGmtrbt26yd79XVrn2D63jXWByWSCkG9/lXinjqZx0MzP3FungYh1\nU2Pbimq1IhU5CEHeLX4o1dlFwboaY8wZ0T5ARwdzNLYiSzOcc7TOxpo1kWMAp376gFbxz6fa0BsU\nFFnK0fEJRpvYsQ0txyfHSKXRJo0nTCVBqK5e7dHyYWMxWl7NmSsO50BKrKsBFa/8MZuPIGWE6giB\ntTVaCWy9wj2mi+mz73kX7/u2D1DOTvjpv/FTfOG3P0VqLesmYXxpm/rBEX0tyKRCCh9LcCohyVKU\nTNg/OSTvr8WTJx2yUgRkiJsNIhC6DdAGjwkRTymlYGEEL6qaV+ScoejhlEQDhco49BWr/QknQqIQ\n+PMDtgY96v/jn7N49016usfgZIPo/ElQeRpLN1qiVIoQUJVLqrJGEEjThN29E9I0jXI8Z6Nsqip5\nsLuLc469vf1Ykuis39/+oW/+ss/s0dpJO4fMKYU9eBehEs5hjIR2xbIpmeNZVXOCCfzJE8Xu//pR\nrv+pb+O15YKynDE/PmFnv2Q01vTSMavZ6yRK84Gvex7ZW+fczee5//mXyfM+T6xLzvcNeXlI05QY\nI7FtjbdN187t6EESCG18GfpD/tkvfZS//le+m/291/HB4RuorUdh0dUMu1zw4v4BL33yBZIkYX1j\ng2I4JN3YIu9fJOtZQl9iQ4tcBqyV1KWnP9yI4YAqJinmRUrdPH7SqLezwK5mE774hReoVguu3bxB\n0eshUo31HmOj6sH7NtpHkwRtNLZjg4pOrZFleQzB62JiTlm3SsmzOpcgXr/zLOPGtctooyiyhF6e\n8fJrt3EdJer8+fNkaRaD4uAPSdGklGfUMH/KfJC+ywcCQtwknHMx+I0YURLZrZ2HTshO92xpyxXJ\nV3DMvJPH0XnNK//jP+aX/uE/Yhgs5/INpnbJzXxEM/N4IVlLUzIhsd6SpSmpUJg0QaHJipxWBtCB\ntKOAWTzWKUyX8BtnwEeimws46djrBz5ZH7CzmiJbiTeCYmudqvXszk/YuLRNvVjQGw4oVzXzN09o\ntyTJdMJgvsO5q8+QXyri58JkyLaETiKXZQlKgnRR+2u9Y7momM6mzGZzyrKmrlu89Wc3K4jrkdYa\nL0S3kX/58WhBJ9BRonxHOm+BSGgqvUfaFdUsks5H21tsXBgRnn6C6jOf4Xd+5eeRly8wOdrlfpZx\n7+4Ro2eeIEs2Wekxt27cQuQZo81LmHyDi/srwtUhcrrDwGWsn7+GFi22aqgXFSJYvPAEb5AqIYiA\ncRVKetomgzTn7/39X+An/5M/T13N6KU5IdUIpciTlCQxKCwPdu6ymExZLBYoJVBG8/n/53cYrW/x\nrnc/x/kLF/ngt30LBEXjBc5ayqZmUa1ovEeaFJP2H+W0fNXjrZElp4tesBWr4xV3vKWtWy5dv8Zo\nbYwxgsZbNAJXVfi2JVXxvKe0QXcNPiNjdlZlY35TCGB0Agi0FkghIpQ7TSBYeolm3MvZ2BiSZQWu\nvc9wtMHx4RFFPuCJp24hOg2skhKNiN3kEF/w1rWRBxD1T0jhQZyChWOuU6JMFygn8EJhrY812O5l\nCw5c3VDN52jz+LnZAH7m2/8Ce3dfpa8kqTAQarbzHItjfPky/g3HmtEYKahdS0/GK3MqDSC5MBgz\nWS45NxoCEbQeQrzyB+9jlBCiq41DaQKvn8/Z8zNOJivK0iOzlEW54GrYolyuED5QHc0Zjtaom4q0\nSJHK8IU7O1zeWmOyvM9s6VBmRNYfkunY8IVAU61o6hXLsuLkeEbdtNhuUVUqQnS0NljXAAHnXdSY\ndlFD1lq8hCx/+/l8tDVTIaiW8wgP0YG2as7E7kprVD9jbbgGQRDSjMbkNHqdp9/7jQyl5NMvfI5z\nGxvIENi5d5vNAq7dusKVW8/SNoq0OEdJtC+21Zydnbt83Y0nufn0s2xcexJb1zgEwVqsbzqivkQZ\nQRABYVsgoLIarQyjrQ1+9md/k1//1Q/zL3/x7+FqT5IWCGHIspxLT9/kuQ99iMQYmqpifnzEF1/4\nDEe7e9y/e4fl9ACjFB/51Z9HCE8rEvrjNcZrY973H7yfq7eeIumPqOzjmWj5R0fbttRNjRCCZjVn\nfnLIUZGSFTned3bOzr6bpVmnE43+9thwMlF0D6RJgtGGqqrOaplaRzG4bS1SwmCQ8+6nb3Jpe52i\nyJEqJ80yrLVsbGxw8+YNijzvDCDyoRi/aziFENBKg6CLMIlaVk4bTcTEBX8aX+IsQehO7+w4vfM7\na2nblrt37/KujXOPbgK+hjE72mXLFKR5Qtp4rvU32aNiKBXZ/jFPmJSQGYyNt0ljDN26iBCBLEm5\n+2CHi+M1fAAl5CnfEjpJWmYD0zwhOM+LdsKnThZ4G5hWntR7QgODYp2D4wlpYjBJiguCyWyJVILp\nfEHrNW/sLnntbsnlzZTMHPDqS6/x3T/y4xyXFYuTE5w/Xcy7xGDglH3pQzy1Kik7B55GKEiUZLFc\nAhIZQBkN0tPU1ds+s0erMw2BumnwQnDq1YuUoBiHkOpBjEQg0uiDt4xUiqBgdPkq5pOfJAmQGYN3\nNbYt0UaDVihiyJkInslkn499/LeZzBu+89Y3cO7We9h84plY25IGKaPMJXRaz9PGR3Cxpqu9w9qW\nLNUMhgO++3u+j1/48N9HBhvxby56/4MvkbJP4zw677NxZcjz/RHaO1564bPcef012mVJL9EM8xQV\nJMvJjPlkyptv3OXy9Rs8+/zzPPmuZx7ptPxxDmsjZalcrZhOThisjXFtS2Iy4omhijnqzpGkKUbr\neBogxJOfBakNFtGVwTSJ1mcdd6NTrBQE13Dl/HkunjtHryhIsozDozltU2GkIgRHr1dEvWqIiD0h\nuswuGelj4jSXhoc567G51cYmmo6L5WmtNgSPkhYfDNIrhOyMAtLTlIsIoX5MeabGQ08atAWRJuR7\nC5566iIHh4f013vY+RygYw4/BKd3NZczbXBEYcbNTwWPw3c5aIqlAhla5q7lDxZTgtQkKNpmFUsB\nSiN0gnMRPm2bBm0UZb3q4po1XiQ0bWA+PwGnWB8PQAZ+/Vd/lWvPPM9gsIYMp5Z0jRD27OeMcW6x\n3n4KNwoh0HqLr+JcZkVG6BI06tUczdsrbR6tNKpzhygl0Tp28ZVU5HkWJ0eeOl0imSlNDEoL6mSA\nuvgUb0znPLF9EZFt8tyTzzBaO4dWK+pSkeZjbDlld/c1fvl/+zBVM0W6Hv3hJYpkRHm4R29zjaau\nEcJRlyUITdHPwEXhrjAJ0ntWiwXDTgxeJwlysMb/8C/+d/72T/0o8/kxAosINdY2OFtR1o7+YESa\nFORFxqhf8H3P/QWKzQvY2uGt55UvfYb7X3qFl7/4IsvZDK0V97/0Kgd33uAjv/Kv+Ot/5a89yqn5\n/z2yLHt4tX9L/dRaT5IklKsVRi85uH+fwWDE5vltRHBkSYpWMnaBix79fInwMQ0SXzHIE9rWnUUO\nmzxHWU+RZWRZGoHS3tBL13jq5nU21zeYr0qW0zmXLl3g3Pk1Wguz+YJr165hjO5yjB7Sn04D4RC+\nSzeMC+VpjTT4Tnd6FhkcoTdaaeiMx1Kq+G8dicp1J+qzDLPHbGx5jUk0RkvO2YTpN18mbzyX2jVq\nLdh46haLV+9iCJQGRKdsECJuRArPufOXKOuaIstog8XEw2C0+YbY7J02FT+38zL3g+KazQiFYpil\nOJcwX1VkmWE8XqOtK4S3aBkILvYcpJa0rkYryWQ6YT5x7B8esbW1ztbSYZI+Tzzz9eSZwQdHCI7T\nBFTojBlEqV4Qcb598OhOWWCDp1ouEa6LKBKnwXtffjzaxVSrGJqmJYkRqCQW64WKtPrWBjr8etdV\njX/udAfxowFzb1nu7dLv9cmzhKpaMisb1GrFzr03ubdzl9w1PHHhEr3BOTJV8sonf436/md56tv+\nIsW1D9C2NavFgiQtcHWLNilGJxT9HratWa5mNPUKpEIJBVJzbvs6//Rf/Ao/+Vf/Eq6e4QLYuqUp\nS5q2JlkfkqeKfjZCC1gb9Rms5VSrlqaquXB+zJVz38SHvuNDWBuZrHsPHvDqS7f53Gc+98jm5Ksd\ndV0/1Fy+ZTGVQeF1BLnMF3OqusJ6i5Dvpkhz9Kahai0y0aS9gnG/wAhPIGBdhZGa0TCnbBxSGkSS\n0tMJvvPqJ0ZTLaa8773PsXlunV6xyebFDNvUNMs5UgfObQ557tnnuHXrFmmeofRbP/ZxcVRdU6Rx\nMR1ASkljYxqCklEu5YNFCIVUiuBDTB9VKoYgOov3kc1QL1e8/uodxuNxp3V9/EbA008Mm70+w/c+\nTf2x26wXmttWgD1gsoI1B4u+oV/VNEqQEFUQUS7myZTm3v4uT1+5cvbOxgVMUHmH8y3/4P6r7Bw7\ntrcCba6pPQyLMalMmR0e422EyyqlaGvByXRF6zx50Y8U/WpFCJ7hqMfx0RTbBE5Wltouub/zUU5O\nply4epUnbt7Eed/R5Tr9cGcRh7jKx3BMiyTelEV3YsV3Jg31cJP8cuPROqBEQsgVaTDI1CCF7rpo\noTuWR6iJtfGaJZXukHwglGQkJSyn5MMNJpMjpvMZyzt32Dp/ifHQodoVlzeH3LryPG1bszXYZmvY\nYySvsjCW2eQBYfMIjMRoCTjqZkGoIC0yhAzoNKGeRl2bFyW5kh2HU1D3R/xPP/ev+KWf/ed85lOf\nABto6yjmlt6BrxmvjZHBce3aBQZr65SLGm9rhN0HIUhMRt00tLVDJU9x/tZ7+OD3fu+jnJavafzR\nrr4PnqZpKE5jTJxnd28PrQ1Xrl1GZxlpkuAcGB9Qwx7SOJbLGYPhiPXRAFu3zFcVUhuSPKepa7Le\nEF83LBYLLl68SL+XI2RC5UvGOiPXObO6oqk9Os944umb9Ed9jDYoJbuaKNAZCKQE62MTwnVNB6Vk\nR9KH1lqQASmSGADZAcB9AOmipjSEyHAol0uapokyLsKXe0zv+HHsLZdMoNEG++l7jP7mdzH9J7/O\n2vlz7N5xDJNjfqE85AfDBaqeRDYBoTueAVEYYxA4KbAuxgI5LxBBIDG02YzfPKx4MG2wvqafbeJD\ngnWeNBtxcnICUuNReFvRVA1CaoxRuHpF7ZZkWU5f9FlVLYf73YnVWXztgJJl03J0csytp58+07CK\nEM5wm390+DP9skOEgAgeBXjRMUAcIN+hOlOlNUVeYOpASDN8FXfxmP8TOgp9TLyUQpCmKQQHXbjd\nlpKczE6o8j3SJGVjY539kwWf+NTH+Y/+3HcjtreibiyRaCmo5ivu3H2NC+cu0eabWMCdvE46WMcE\nhSbgnWUxPUEnKQKBNppBnlE2FU1dI3VCmika22CEJCjFD/zET/D9/AS/+DM/x9HBSwyHgVExIMtT\nUiPY2tiOzYtWkpuMsq4j0CVLsS0YnVMul+QiAVIW1epRTstXNb6cNOpUdmR0bCqNRmPquqZu5hzt\n7dAb5BSDMdY5HIrRxjb1cg9TCEabI3pJj36W0LYVmyqhrluaykFWsJxNwVkunl9nfdSnWdWkWWwk\n1MsYO1GuVrQonn3PNzBYH5MWRfRgd5KrU4xeICCVJlEC5yJsxnZX//l8TpbFnCoknYQv4gJlx7iN\nRoJIH7K2ZbWKeVVBSEz2+BkwAOTTFynfPGYsa/x/+M3Uv/y7nLBgZy+wvZ1wUjt+uL3Ga9UhT8qC\nNgi8FJ0rLF7n0Z4L29usmpoiSZHiNI8+oMs1Pla9QlUtCQLyIieIGCF978E9TJJRFH1UoqlaSzEa\ncrR/TNJKzm2to0zKbLHE2obWttRtE2lyIbBcLhkOehTpgA9967cwGI4elnKkgOAfNqIguuqCj9E1\nEGPbvaDfH7O+vsFieszJyQlKSJx9+7LNI/ctyu56JaXilFt6iscCzsg9QgpGg2EEaeCwVUmRKVIc\nw/V1dDFEpT3G4xEb62tMJhPyokd/MGC4to5Ke2SjAQeTQ15983XO3Xg3IRlQLif4uiJSSzxGy44r\nGh96cLHW0rUDY/Jo05xZHIP34APCww/+8A+yfeESWisKU1CYjH4/QSeaYOP1xjtiU0sbqioCGcpV\nQ1VbWuVBCeRX0LK9U8dDBu0fHmmSdgsX9Ho5o1E/RpUQODw8JDjfeZ0CXkbmZZH1yLJBfDlEbEB5\nBNIk8c+2juODAy5cOE+vKHDesaxLnAu41nN4eMDJyRSvNMooityQJkXkKfjYvQ0BApKgNCQJHvEW\nS2rUsCIcurOwtm0Tm5VCkOiudtbJfAjhLIytbVrKxQxvPUqmKPn4sWkBrHTMgsUJh//4F6jFgAeq\nzxNWcn5tnVYPWaUt54ohdar+EIJQCNBCoH1AKclktTrbvISAKgEbVIRZBo/WiqZtaGyNEAGdGESw\nOFtRrZYRgRkEbduQFz2U0szmCxASaSIUZW1jvQvUjKfLpm3xwUfE41uiiULwZ4e0Uy3pKYe27VQk\n/X6f7e3zNFXNG7dvc3R4hG06Ir9/h17zhRQ0dYORSRTZdvULpWLmCt2R3LmYd+1dQGhN0i7Yvf8m\na/0MywaNkGxff5LhYMA//Mf/iFQrFosZR7MlQiqeeuZZ0mKNuloxLAxMVhw2n+LZdz3Jcu91tNWU\nxQCBICsGDMbrONfiXYPRKa1rwVt8G7Adrg0CSgpk0KCjB930Un7wx/5TvvTFF5jtfJYsM2wMC4wR\n+LahaWqcg1VTUbmA8JplVbEqK8rakyqPNy2NevwW0z86ZBcsuLG5ycHBARAYDPsMBn1OJgVHh4f4\npuHwwX3S4ZDBcIjKcvKsh60C0mQoGViVC/ABS2TKHu8fcLS3z7VrV5FaoXRK3dRIA3SQZpPkWA9e\nKHrDMb3BiDRJuobRqbMpnlAjV1YhvMN119H48ysCkTVQVzHU77QurLVCqEhCMkmCD1GV0JYVJ4fH\nvPba6wgpSXtFdFg9hiMVsHMh5dljz0475aJRjIZ9vtDu8h2v1Vz94e+k/IWP8AfZjO9scma+JSc6\n905ND8IFUgGHkwmFUmyOkxghJDR/YPeYHd9DJQWFtqA0q5XFeom2HmEbiqSmsTVtmXEwOeHiuW28\nb9g/qtm+cIHWeho3oxj0cUJzdHAEOJy3TOcrvu7ZW0hidpi3LtY/RZfM0dX2h8Mha2trPHjwgLop\naduWyckJ0h7Fz4d1oPQZ/Lv9Cg3FR7qYpqnG24DDIVJ9hkCLyYDxa852leBQ2uCFpDy6jz3ZQfjA\nletP8NK8RqSGL71+G68k87pmtXMfoROKvNch1CQmLfBSUFZzPvqvf5aPfrTPd3zLh8gSgVYVzoFt\nFxRFgrQ9mnqFFJ7QWJxtkToBZ/FOobzGNTbaHqWJAYAovNDcetd7ue1WHO4/4MbgAuVyH5TtdG6x\np2ZrT1NblnUdrxzCkCYpdV0jH8PTzFuv+WcouxA42N+PXAk878YAACAASURBVNLgmc/nFEXG9vY6\n3jc0VYNraoyMmtGyXLEoJ9hqjjIpuUnwtuZw74DZqkJJzWI+Z21tDZMmlE2NqypMklIMhqAkeb8P\nxLhoLyRpXtC62CBDCoT3pEmKUgqjNUEIgnORgChlPCl3NyRrBUpp0izWAZXSne41XmdF8DRNiWta\nymXJ8cERB3v7rFYN69tb5P0eUj+ei2mdGKzNuOdaRv0FZvw0l4oWe3DAznM3Gf7yrxF+6M/wzb/2\n23ypnXKtDUgVcYkIqGjpBTgWKe32JT65OOapjYvsb/RZDYcU56/wpE5Yz3LGG33u7NxmOBwxW1Q0\nTiAdWCQ2aFbzFQKN0ob5asGgGHN0OIvJDsFz9dIlPvvCS+RZgtaC8doAo3OGRRblVDrFK0Gv1yfN\nUtplxXK5xFnL8dERRwf7Z5bkDvmBlyo207Q+Y0F477vu2pcfjzZQr9/veKJEYEWXNx+8xyQmajhD\nTPbUKkNnKRLP5HAfaWu0VsxqR2IU5XzJpz75aeiyoE7J9bFTDvPFjEE/Irge7N7HOstkOuOFL7xI\nkeZsnE+QypyRo+p6BSQECb6saXGkKqNa1ShbR12i7CODpq3b+EuYgNAQgufWe76dW97jV8fc3ztm\nPKgjsFgobOtxDuq6pW4idsxkSbSU1hb9FTBfj8MIkpjqiUcRLZptCNx5fYfd/WOefffTjMab3L/7\nJrPlAiYJ6xeuMp8vUKLH5GiHYB0mTRBScndnyuFkgRBw48ommxtrVE1NjaBtWvoDTZL36Q/XybKM\nRdmwWq1QWrK2uUmaZRGdFhvD8YZjJCEQMXxIQnBni+jpRmBMikBC8F1MSh3dMK3DmAABKteSKs3+\n3gFfevGLHB4coKXBnEtwLqAfw40RIE37FMk6n7OHfMu05fe/+Ls8b7Z57v23+OIXX6fVPcyHP0KO\n4QqKlpp7JTzoGxaZoR706DuF1ikEwSpU3MtymkKRti1Be97/9C1WrePugzfZnc0o0kAQgaYJ9HsF\nUnjqeokMgbJsefPeAVIGPIecv3iRC5cuMSgy0nzIfFWysT5gNBpw/vwGe7vHGBkY9jKs1pRlxfSo\nBB6eTCHCwL3rmlPy4UoZ/MMSwKlKxXVppW83HvE1v6PydJg2Hx6ecASxbuW9iLnrLgJcRVNSyYz8\nwjWOpxMcFZXuk/qK2eQI0ekDg+jSLokP5XgyQ6uAtxVHJ0ekSQI+cHfnPnma8R6j2djcJks05WpO\nf7wBSlOuSrSr8V2Yn9Tgg6Mql+iu1pakGUEKggDbxoddlcf4ENBZyubNb+LojX9Lnsddsg1EeLSy\neNcSiDY728Zr5mr1+DWggLd88CRpmnTyIYc2Ol6RCSyXKz75qc+RZZIkSUlJSVpPnudcunSRycmc\nbOMqJ5M97r55j+l0RpJoZJZGsb7SzGZTtEnJsoy19S0uXr7C+tY5eoMRZVmyrEqqpub8+jkwCmE0\n1llwkQUR6HKexGkSbSwvSdnJugRnC6vWhuAz2jZu7E3b4h3Y1hGAsq3ZPZ5w98373L17l6au+am/\n8bdoCfQSxeTk+FFPy1c1jo9qJv6EZG65PZ/w/sE6c7XkwWde5oJtSZzmlZ6ilXDS38JdLAgUZEaQ\nikCmJT5VtD6A9eRktMrgpCZoQR1ivE9hNHleMOr1eeWl25hUM966RvBwPJ8SfGBjbcS8PODgaIZJ\nNOe3trh5/Sl6g4IHuzu89vJdiqzHzSvrXLt+maPJPptbGwyKgjdff5Xx5mWceJjl5d+ihxb8YQu0\nDxHdKJC4TrKYJEmMGge0eYeK9uerFVnRj66jDnRy+oEOxC5+21qMNh20NcMhyIcbYBRlf4O+Vvjp\nigcPds9o95HFpvAd03I2m3N0cMxqMSNJYb5aYltPqhVBKO7c20GmCV//fEKS9XGhZLGcs97bIB9o\nDu6/TDrYoCyXeOGxwZKblMUUsv4QCJCk0S0nY2qqddEd5RUMBiNGT38rD+5+AW1g1gaaxhG0QhiQ\neJJBbLj4KuoxH7cRREAEEX8HAm3bGek7nYw429BjPEVpBXKUo9FInbGqaka9DNSca1evMh6P8MsG\nRWDV1Jwfjxj1ephM4L0lTxPObZ1nY32DNM1Jkpz5fIkQgpPJlCIvQGiUSPGNw+sOZBI8yEArHDII\nnA0k2iAQXU2+k8EIiZcqEqVsi5SK+fQE56Bp/JkNsW0aPv/5F0iDYbGq+L4f+AGa4JDC0BttcHx4\n8Ahn5asfIk3peYX2gddTSYUGpThWgXzzKmkpca4l8dAahRGCSi3woR/fZQehckgV7bqic0alTTzt\nKROTa23wnNs+x8c++3GkFLRly3J+SHD97mASWKwqQgg0bQWyYO/kmP/79z9F23jOjVL+xAffx+6D\nHYokcHxyTNNGiMmqXJEvCtJiiekNzsIRZbeAWmuRSuKCjamyXQNLdfLHCGHR+BBTZwWcXfm/3Hi0\n3vzExBdN8BY1XieihbPO6mmNKv5fSdYbUAdL7WDYLwgnK165feeskwgggoynRm3wzuNsy4PdQ7QR\ngEdJReM8rWtIvGd3/5CXX7nN1tYlgrfYTpzrOipRCMTSg29xnfBXEGjrOpYHfKBn+h3aMspDYgZ3\nJLcJWWDyDcoq0mmausEFT91JLfoifr0P0Zr6uA2pNME6siyj7ghcp+vn212NlqslWxcvk6Up3jkO\nD4/Y2t6mrSpGoxF5r4dS26hJjI+wrWFtI0JSrl69RW+4hlaKXn+AC3EDLauKouiTZhlJlkUOqo1Z\nTKcnEujoT2fSqM4FKU9xfpwJ9621naJAU1YNrvVYD9Zajg6PmJycAHA8nXD1+jWuXr/OarlkNMqZ\n7D94bNNJlVbkjYAk0OYDZo0m84JtKxBTwVLUtEWg0YGs1LSFApsQVMDjo1VTiK4fEG+aQnc8WOJ/\nF0QJlRCCPM+Zz1YIBE1VoXXM82rbitbGTn+SJgQPSuTMj0tuPPEEwi8QwjNfzFjbGqGMYnYcm1JF\nljAcjEhNgu0CFn3wmM6KrJTAeYc8pY91N2MpiM1IHpLKnLWRHvYVauCPtmY6GFAreSZNoPvhlVLd\nAhhfzoeyC4UPCpH2CJN9ekUf0+/h2SGEQF033UOSXD53gb2jY3pFj5OTQ0SoyEy0DMZ0UR6mFALH\nxxNWqxf5hq9/H/3BgOA9mTH0spTXDqesYUjSQBCG3BTYAFhLqk9z0zXWWprgz7zAdJuE0RIXBKZY\np2oc1dGUcn5AIw2tUDFpQMrOvz6lrupHOS1f1ThVXyyXy6jl65h18m20eSGEMx5DnmeM19aoFnOk\nUqzqGt9ann3PczRNTTWb460lWI9wjsG44Kl3P49XitVyQZrlmDRHes/+4RHFeEiv1wOlqTq4r20j\nYFoUBSsCg/E4vuBdXV1rhUTF5NKuduYcseYrNZaW6bykWq6wLrBcrbDWcjibkeK5d3DA3/rxH48s\nAAXBNRwf75Nl6b/PafhjG8YYtLdUpiERGoGiNgIrNEbAeGubxckk2jMLFSHQKgHpY2/Ad2U8EWvm\nUkgcLUJoCGB9PKEkSuERDPsJs1mBczVZmlKuKsw4IwRJ0zpCkARatMlIE814GBhkM7avXOD+zh5G\n9zg8nNP6ktZp+msGgqBtFkAPRA7BoroN8+xq70OElLtYA9dKdgcoAaKLIgoeYxStbRD+HbqYmjSl\nttFdIlUkBKVp2mnFoiattdGBkqTRhx0ECK1YuZL+5hrOtVjbcnhygu92HuHh7r038SJQ1vNODhH5\nk7LTAyIFuhNvOxdw1mGdpSxLxusbSBlPS029ZDo5IM0MrQvopIsRlpI06UecmA9I52NzSioqFUEe\nyhikMQRWKJViZMAvlphywnR+SDLeQiiBSQ1CCObzOdPZFPs4WhBdXDBPF8nT+qlOTafrfBgDcjqU\nl2ysr7G9tRH1m0nGqy9+iYvXrzJfnXD/YJdveN83MhpvopWKi2YAnXbR2EIwHI1w3lNXLfPZlCzL\nUEJQr8ooHI8/FCFNMCZ+3PtJrK3qLMc7R5qlEajT6ZkhBr0FH4MRnY181fHaGtn2Ofb2Dih6PQDG\nmxtMdu/zZ5/5OlZlGclXMsH7Fm00+VdIs3wnD6MkZBrlYkxQIuPtTIoIcL507SpfOD4hXi2J75gS\nOB+vzvGUbxHCxEgg4cHFmBnrA3SgGack/aLH1vqI/YOKpWvwDrIsp64btNTUZezmjwZjnG9o/YpL\nW7fQieZgd8rx8YJhz7A+zOhlQ5xMaWYnZLmh7fexDjxVd/KM0JKz+hMB27oOdAT42NG31pIY0YGu\nPHVrOySkettn9miv+WmCsE0EAvtwVtNyziO7tLLTSN0kSaIMSWuaVRRon9u+wMHufZyNomrnbNSS\nAfo04K5zOtjQWdrsw0JCEA9TL6XQOB9rrMYUmCRHK5hMDlitphwdCvJ+TdFrkEKQ9YZI0adpLFnu\ncb6NyYZSxDjoJEUQUELivSG4yOZsy5Ly+JDjN18iXx2yff39pNkatrVMJidgowTscRtv9eSfLppv\nlUi93ZjNZhwfn3Dx+g3uHR0htYoEqUEPu1xw/GCHrbU1lADpK5TSFOkQ3ctp6gaCpW1a3ti9y+zo\nkNa77u+W9IoC6ywXL15k0Ta0bYNUinFT0R8OMeFhGqlJ4mfs9JpvrYMgY0SND6zKksuXrzA7mXL9\nxg1WZRSizxZzjHd83Xvfi3eeIs+RTtH4BcWgj0oev7mEziikBKlK4nvUOEKwCBWv6P3xkCBPkXt0\ntei4iAbhaW3kK2RdZpdQklq0JNqwNh6CD9ENt5xyMD0ik1CulgShqOo5uQo4J1FphjYpTdOQ5Dmt\nNbjgeXBwQrVcxSggH6hKSV5cZHdnj63NTXrS0usPEVJTW08i40lMCHnWpT+9EXdn1K4BHD87py63\nQOTeRuZp7IW83Xiki2ndeGzlogulo81Y6zAmhqolxlB3pzTvA1pHB0pdV+RZwdp4g+ODvXi60JJ8\n2OPc9gYvv3YX6x8WipWUOB+78WfMxRBjU2LHTiC6Dkmepght0CbBuYrf+q1/w2/8xq/x/Hue5+ln\n3kNZVTFXpm5BZhTDTYQMSNVFWABSemTwGClwtgancMHTlFOOZzusmkPG44LjgyOWxR5msEW5qhEq\nZTzoYc3jxzN964KZFhlSxDpU42xMDw0QulOf7himIQjyNGUyOaEuSza2t+kPhiwmxxwdHfHkE9cI\ndcXOzhvcfPLdpL0+eZ4juxwobxsU0DSWVBju3tlhvLaGSTVCBpwJrK2vs1osWEznJEnCqlzRlDXi\nssDVDUVeoE1kPjT4CEERIITp6q0WLTVZ2iMYw3Bzg9VsjtEqLrit5flv/CZm0yWXLlzkwb17ZElK\nvlag0Wef38dtxK51wHbNNqUk0ph4C9NQziboND4f0b1XIcSFKS6jgcQYtq+ex1nHbDKlPJnQhIrD\n+QItYoBlQKN0oK5WjPoFq1VNjcO2LVnap65XZ2vBcjYnSdPYCLItAceqLEmNJlMD9g6OUVrg6yWX\nnrjCsJ/QdPZVgYhyva5+772Pze3uthJBJjKWIJyN7IbOteWCQzpi+U6Kt3tkj3Yx9UFAEEgnCIk8\n66adQn1OdwXgbAc5taTleS9m+aiE4ByXzm3EDOy2ZX004OBkdiYgti6gtIm7ZXj4PbyP/mnhfYcO\ni7uW0gZpFG1bcfv2K8ymC9588y7rm1uMbEMpJWsqJWvjQhydM/os7vmUz+lsdHYEZ3EhFsCLXo80\nT3Fk5MZxuLuDG2yge2O0zpFKdE2yx3d45wni3wVKnMZ/+NBZCxEsFguG4zFt0yITg+kkVViHTjN6\n4w0SFeHRSsfTgQqxnOO9Z1Wu4mlmucK3La6qGQ16EeHXtmjvY811XuJFQ5A+utp8YJxm5HkeQRxW\no+Ihp2PnOnzXQrPOUZYlBocKUC9XtG1NWa1QJkfpjIsXRpSLJXmWx58z+EcMC/4ahyBe3YxAdzJq\nuvfGec/+4QEIgdKa4Hxn1Yxfp4Tk8sULOOt4cPtN6mWJJMYyoQU2eKyMN1EVBIKG9Y0RcmeKVALa\n6DyLZQK6TLAOlxfi93G+xQcbs+5bi3OeTAgGvZxBL8e6hrw/wDeiS/KwZ3Zh1bnSThdS5zxKClwb\nGaZSSIJ0+C5JN3YgZaywf4Xk4Ec630HGyWicAyfOjt7w0OMbqfsKkSUxyE4rVC+nl8faWKIUw36P\nPL2AtZbJbM64SLi6OehqI1A1ji+9/gAhPK2PXvDTkC8fArr7pxIxqTDLC/Is5/Xbn+f27deo6oqX\nXn6FF1/+EufPn+c9z72Hb/rg+fhyhhXaD1Ghm7TgEThEU+FsgxMS6R0i69G20FiDc5oQciblCSt3\nyMnnP875m+9FJhleOpb149fNf+to2gYpop30lFQv31JrCp1Bw7tAU9dUTcXu3gO2LlwgIJgcHdDL\nEvqDNVTaY5BL5osFuq6jlnQ5R0qYLxaUywX98Rov/P4ncE3JvLTUswneOZSQ1LdzZsfHDFWfNM3J\nz485PDnh3PVL6CSNpZ3FiqInMUmG9OBllHk55/FRtkzT1NjFjMOTKTrNMdqQpwOuXL3BSVnTNynL\nyZSgJIN+n7YpWdkV7fHs0U3E1zBEEGclsyAlToJt25iOoBTHh0cIo2jqmo31NULr2N3dBWdRAV6f\nzuLBQjqEjumjIQRoY2lNeEHwiiBKNrYT9mcBbRKqxuJcvD8ulrMzV1WLINGWuinpra8RWtAywbUV\nQilaW5OajFwHBn1FmikOp3PqRmFyBRqM1B0I3NHlysb1piPw+051QNfVF91VM8Y/O6wHJd6h1KjG\nOZACJzyJ0me1jOha8igZpS1KKpTSsQCuJIP+gMwkhLah3x9w88lnWC4XtE3DVtMwnZ2wXM5xTY23\nLUZLnr15Hqk0vaKgdYK9oxPu7x9Q1i0IhZIarVQH4Yie68+98FmWy0V0Q+BxwXN4eMSdO7d53/s/\niHctkki/UlpjjO6K74GqqiPSK7TIJKVaWT7yr3+JN177Ipv5Ap00BAWZGRMQvPb5FxAmFvcf99QS\n4UFpibcPd/quuU8IgSRJY1NIBcrVgs0L20g8+7u7uKrBNi2hyGhtoNcXHO3v00sNvV7BcjYhiEi/\nl0mGSDKWDw65USr6aowKOa/ceZOjeknbNDx96RlGYkiqE9ZNn6oUmEuXkVJycrBPlmVkeYw2aa1F\nC4lEIjoYBgHaquLeG29wtPuAo8mUwfoWH/jAn6SuKuq2YXMwZnqwg5/NSJKE44MZVb3g/p3X2K8X\n/MCP/tijnpKvaghB1OLKgAOCUSTKMOj1cVXDlZs3+dQnPsn+YkUIFqwj0fE9FSre+LyP3X0hJAFP\n6KzicZmuu3pnIJE6RkIbUKagbdoYBUM8OQohaJoybsRGk2c5IXiyoocNgu3z62z0Ev6/9t7r19Ls\nPPP7rfCFHU+q1NW5m2yyEzMpMSgQI3FmrIEMQ5gLA/bIsK99Yd/6j/CtYV94YF/4YhQsajSipBFF\nMTTZJEWx2WTnWLnq5HP23l9YyRfv2ruaGjUBU4WpLmA/QIPdRKP6nP3tb603PGFQJs5tbYO1dMFj\nlEbHlBdJAde72wkAWrilfd/n/0+M6oMP2cVaOhNjDF3XU5bVB1cBNR0OOM5O2cuJWwghzy7yZj5p\njDWUhWQAaa2oiqFkymhDKko2z5yjGo4lE931DCdjmsWcdn5K2za0TcMghNVDiRbObI/YGlrqwqCr\nirYJ+GgYlAV9J6yCb337m5ycnMhSLEUiia5t2L11C9+1mDwqEJKvxliLLiS2emRKTk9PMVXJyWzO\n7rXr/PAHz3FyuMfRtOL8hQ1Ojj0zd5P9/UNJ5rQVSYM1778xvJcglDEjL6USZ/SUyJemHKx7e/u0\nROqqAlNx8523cC7x5MYzctHt38QvWuZHp5RWUZYD5l6zvbVFjC0xKq5+7wUesiWToiYGzWM7D6CP\ndynHBoPFDEoO+1NOXGTr4qPsnN8mhSQ5UVrL0qgopN2zMqZQ2ZczOE8za+gWPS+88BN2b+zzP/zP\n/xPVaMRoOGK0OaXfPWb+Fz+i6pMkXobI1euXOFEtj37hE3f7MfxSUEZT1zUbmxvMF3NuXLlGionD\n2HC4f4BF8du/+y/40d/9EKUTMSlMloArrQj0crhEUYrFKJla2AG97xhUFu8VhfXMFnN0YTk5PSFF\ncYULIaCVpa6GWT0n7m0xRU4O91hoMZqxxYDBqOb+Cx/joYs1vmsoJ2NQMDs4QBci0449dKlFZ7qT\nUgrnxbc2xkRRGPGQ8AFthOrlgpPu1TlRU3q/cqX6x3BXD1MfZABijCIQSUoRkoSahaDw0RIR49kz\n1QBViLGvUeJ+jjFs7JzBzQeMpl6oTH1P07b4vqFdzPCuY3E84/D4gMXilKQSVVFS7pyla3u6RUPX\nHaN1ZDwckFTAqgpjKj7yoYfYHiXevnaN2ayFJGbAh8cnhBhX/oUhRsmSiYEQjWjBrWFrMOCdt1/h\n5R88x5/9wf9D253ge8eNhbSQG9Ntuv0jaj3g5PAY5w8wpSGEe9NQeImkIKoERmZdS0oaS2pKHuVI\noF5ivnvAjes3eeKJp1hsbLJ/8zpvvfwCV69fp180XDy3w4cefYjTpqfHYYoRReWYHe/x3De/xZV3\nL/P7T32JYVVBCJxjhBkFDtoZWJnzfefG6zz8ySepzlccX3uH8w99iLKqs4O+oiorlK3yT6swCpzr\nSSly4/JV9i/fpL1+wid/9fM8+uAjBANh0XLpa8/zJ//n/82XH3uah8+epzaGjbLm7AMfIanE2z+9\neXcfxi+NxOz0mJPjI2IKSH2TIHpSjm2+cuUSZ85ts39zV55rURKSzEKljY+5VXaZqZOwyaF1Vsgp\nRet7YrugGIyYbG4yv3lMcI6iEjvGwXBM0hVKBWazQIq9LIGyOE0p2JoM2d6MxNCyvbNJDDAYlNx/\n8SI3Dmb0PmB0SWmrPK+X8DxtRLKudVpVwTqnJSudsNpI95yhCKj4Ad3m9y4QlcwunfNZ7SQVjdJ2\npY/WxmBMgVNgEigtQ2nvneT5DAbitmSTqJ7QWCMxXt5VWCoi4EPP6fwUQmQ8HFNXI5pqgD4NdIsO\nU1TiEpOizOSOD7FW8egDFzidtZzOFuweHuND4qWXf8rWGdGDBzdA6QFEi82qGa2Ez3p0uMsPv/u3\ndKf79G5BFEIeB/u7nJ4cU9pNrDLsbJyhdz2z7hjUvd3nC8dQEiiNNlhjCDFKWJ6LKKNYJkUuzb9v\n3bgu7IyDQ4L3tG0gzE8JTcfubkRFz8bGhHK6yWg4QiXFq6+8RHAtjfG8cO0NJo88jT9dMBiPCKXh\n4HjBue0d5q7jiaef4unPfppqXGOJ2GoAyEZXLyWvmcYVU2LpGO86hz+eo+Ydo1TwhS//pqjhfCLs\nzlBv7HL2/AX2ZseMbMl4OGKEoiglEvihybm7+Sh+aXjvIN2u0FO2x9RKTGxijOzt7rE1nbJ/6xYk\nTXA+O7RlzwO1PJwUIUWsMZiUSMngs3uaTWB0zbwRz2BrLSnklGINo+kY359y9twFXn5lf+Wbu1IZ\nGs35s2dkkZUUfe9JSXFussP+wYEE71lD055QFhXaiAMdACkRos9m9DqH9IlBUkrgc2Va2EIO2yh+\nw++Hu3qYalviFi2lsfLlVRpbZDJviHgVsuekFkpEDthbJlM65yTTRRtx+FFimWbKQAwj6vGWcE97\nx2hri+n2BscH1+gWpxyeHFGPNjl77gJnz5zl5GhGOdrE1mcZbmxw49o7WDq0dgTfMyphsDliZ2eE\n1pY33/gpTz35YTa2z1LWA5JWDOoBfnGKHY+JJJrZnK/+2/+Nt195AQhEW1DmAXdwPS60OD2jd47J\neIL3gelgcjcfyR3Ce79waWWj6MPtly3lpVRMER8i7vSEV3/2YjZwFo+GzapnMT/laP+A8XiCO54z\nMRVNd5Uff//7HB7uE0NiYmt+0uzyvR/+KWd3zjA5qnDOcXpywmh7grmwycd/81MMR2O2z56nqMRk\nOrhIPdJoW+DQ2JhILkjXESOegN89ZvbaVdrre/zav/5dzu7soIJn/0++y8Ab7q83+M2nPoPv55yt\nttk/2uOocwyUFABW35sKKJ1ERplWoywDShMikhZqCq5eusZXvvLPeP311yjLoZj2LJeLajkYlThv\ngyECi85xfDrnx6+8RlENeOy+DXYmFV2I7Exquq7lwFuqYkiz6Ni9sYfvW/b3hKIWk1/9N6wtePLJ\nJ3n04fuoioLhYEBVlgxHFYUesjU94mDPsegchR0IYSu9Z/EE2Sw6rlRRLhu3p5hImOwYlqlSiA3j\n++HuxpZUliKUq3mpVhINkWKWs1mbHyIUZYlSerXxPz4+Fk5ZFAu7hKiYYkyrRYdQNwoSivHgHGYw\nwYzP4oOj3L3G/vXL3LjyFmcuPM1nv/gVuhigHDIYTfnGX/0BpQ701mBiKXOhCCY6YnTEEPmPf/Fn\n/JYZ8dFnapISjuKkHqKMQWlLoKSupwQKdEyYFDD5oiiMIaWCEBWF1jSzDgW0/fHPRSrci1hVJCG7\nmqtlRIjGGkvnXdZqW1kO4Cm0oWkWtI2IOYIPxJTnrnVFLCwz1/PaD37IfDHHNw2kJPrt7JBureXW\n/h43SFJFKMVPwj6//unPUY8mDMYTTFFTVjUhRGxZZk8ckS93bQshURhD3zpU03F0Oufc5z7OZv8U\n5x+9iDs85fq3X+TCMQw2JiJB7GFxeICejtkyI7RK6JBllObefJYxKbQxuK4nqiQ+r0vuE9IVHh0f\n0Xdu5UG8fDe1VgyKAcF5nBOe7cqlySoGo4qPfvgxTmdzQgwMRzXdyQmDyvLoxR2M2mex6NmYlDTd\ngs2pqA610mhTYq1ha2uDB+5/gI888SjEhqIy1HXFdDpmNK4YDSqsPc+lt4/RShJuvZ9jTJGJ+Yqy\nzMGdziP1qKKyhq7vZaYfI7YsVi5iRE3XfkDb/ERAiIoGTQAAIABJREFUW1AYQihQyubW3RBiL79g\nSjSdZKobrTFaLOq0ES6gc06ieLM8zGcSvs4bO6E9RIzWjEYjqukmCTh75gJ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Y39/HpB5b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\nsLmcgMq2jAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "qHlkjL7SP8nj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "kappa = KappaScore()\n",
        "kappa.weights = \"quadratic\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ScieJVGfP8nn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = cnn_learner(data, models.resnet34, metrics=[error_rate,kappa])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KvtgdZ-FSjMP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "89ff0834-162a-48f1-8666-835f92f1b85b"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "print(torch.__version__)\n",
        "print(torch.cuda.is_available())\n",
        "print(torch.backends.cudnn.enabled)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0.1.post2\n",
            "True\n",
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "GmbWiooIP8nq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "df50de33-a244-448e-b349-70acd056d596"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "learn.fit_one_cycle(1)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 1:19:29 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>kappa_score</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.553741</td>\n",
              "      <td>1.418301</td>\n",
              "      <td>0.674327</td>\n",
              "      <td>0.205063</td>\n",
              "      <td>1:19:29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "8R_lkHnhP8nu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "e4c66e5b-866c-48c1-ae7a-8ac8397a5437"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rh7Tzwe9dg30W3416nFdSiy9+voiM/Cq4aSzw7L19obEx7eIKpY+/x+Jjjzum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\nvImIiCSG4U1ERCQxDG8iIiKJ+X8Y+5hecgTmIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "yp1PycsVP8nx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ab709851-fe44-42a1-bdd2-e431a595e128"
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "XAun0JZ_P8n0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "0ee45653-5c30-4987-d028-c5c6ff06e24f"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M4nbz5s1LHPiVzpLZB6fTqXnz5qm+vl6VlZU644wzdOutt3b5iO+e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\n7gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "_kg_hide-input": false,
        "id": "3XypsXP_P8n3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Can we fit it any better?"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "XDoEPhbCP8n4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "95fdcd4b-4034-48b2-a439-d53d44e284ab"
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(3, 1e-5)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 38:42 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>kappa_score</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.455230</td>\n",
              "      <td>1.417969</td>\n",
              "      <td>0.675184</td>\n",
              "      <td>0.202684</td>\n",
              "      <td>12:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.451701</td>\n",
              "      <td>1.417019</td>\n",
              "      <td>0.674498</td>\n",
              "      <td>0.203103</td>\n",
              "      <td>12:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.451476</td>\n",
              "      <td>1.416872</td>\n",
              "      <td>0.674584</td>\n",
              "      <td>0.202470</td>\n",
              "      <td>13:10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "dPuaV25_P8n7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "ab983671-3a34-4d61-c67f-e5ab389a47ff"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X75NT6LtHsh7OKedPr+0mt91I+p0NJ3j+3QOs+eCQ+3NZuRX86vktLP3bNvfC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\ngg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "uyLJFSdFP8oB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('stage1')\n",
        "data.save()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "9lI3st3sP8oD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "I8iylwLE00IE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "3d91e3cb-32b9-456e-e184-2acebd10a910"
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(3, 1e-5)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 41:07 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>kappa_score</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.449382</td>\n",
              "      <td>1.412179</td>\n",
              "      <td>0.671497</td>\n",
              "      <td>0.214894</td>\n",
              "      <td>13:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.441257</td>\n",
              "      <td>1.407540</td>\n",
              "      <td>0.667296</td>\n",
              "      <td>0.222894</td>\n",
              "      <td>13:46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.436857</td>\n",
              "      <td>1.406756</td>\n",
              "      <td>0.669268</td>\n",
              "      <td>0.223164</td>\n",
              "      <td>13:56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qXl71Qsf-hLo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "ca6f40e2-0c5a-4f63-a7bd-5b98c7c5a2a4"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Iq6shmShXWVnJunXrWLNmDUVFRY7Xx4wZw6OPPsq1117LiRMnuPfee/noo4+w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\nERGRnWBSJyIishP/B8XQWBf1A280AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "H5WC-Bv9-tme",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "380c44f8-3929-451a-e7e7-d690bc1fc425"
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(7, [1e-6,1e-5,1e-3])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 1:34:38 <p><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>kappa_score</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.431561</td>\n",
              "      <td>1.402670</td>\n",
              "      <td>0.662408</td>\n",
              "      <td>0.229746</td>\n",
              "      <td>13:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.420139</td>\n",
              "      <td>1.398113</td>\n",
              "      <td>0.654519</td>\n",
              "      <td>0.248791</td>\n",
              "      <td>13:41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.406657</td>\n",
              "      <td>1.394301</td>\n",
              "      <td>0.652547</td>\n",
              "      <td>0.255345</td>\n",
              "      <td>13:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.391915</td>\n",
              "      <td>1.389629</td>\n",
              "      <td>0.651689</td>\n",
              "      <td>0.261585</td>\n",
              "      <td>13:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.377388</td>\n",
              "      <td>1.384869</td>\n",
              "      <td>0.647316</td>\n",
              "      <td>0.264931</td>\n",
              "      <td>13:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.363375</td>\n",
              "      <td>1.382777</td>\n",
              "      <td>0.640713</td>\n",
              "      <td>0.270591</td>\n",
              "      <td>13:14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.361168</td>\n",
              "      <td>1.382507</td>\n",
              "      <td>0.641742</td>\n",
              "      <td>0.271607</td>\n",
              "      <td>12:54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9EZStGDJUtnR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "d9cb6ac4-699c-468d-9bbd-c23d1aadcff0"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K4uoWymtbeevLPN7ZeBStRsXyW6eTZDZ4u3hCCHFapKUuAlqMSc/KO2dzx7fG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\nfuxDpycAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LxYgdxKMU5en",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('overfit1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "J11_MdX-ex0-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AWGrSxMqe1Ue",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "2d54d24f-281f-478b-daf3-24a098a25804"
      },
      "cell_type": "code",
      "source": [
        "interp.plot_top_losses(9, figsize=(6,6))\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/uhk9erQ5+uijzb333tvlHaVSyRxxxBEmjuMey3HrrbeasWPHmnHjxpkf//jH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1HnIn2xlwox6KJAJA1BC7nziIzq2lgL0zix3x+AzvmH6/WJx8cnxWydY7fbcTlf+PD5M2/v3yBG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\nQ1S20TAGSlkMnhJSi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Dseefr8yFwKISaKZLUUXiqtCM+PZzvXSk/Na2Gc4qZJ0J3Zamwl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JzYmieYWT+uBX0y7D9aXBxAuE+/3rw7JWsoLLiBRUI6gZ5C/NEKrJVWOa0WDT0msu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g2NjjNMYw7XpWcEB8HQba5RLewkGI2Mq2pAO1nDOpViO7Q5piw8VQykXrnpjShXi82OwGRp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I75zK+Oi+Z0OKkPJgmyIlz2bt7wUfMAq43kQM9C6bifQGqzwztCLgm3sd2oFVDyM6R9w+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\nIW80Go0zpxnyRqPROHP+B3w73Ukrxh17AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "N9F_FlI_e404",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "5c3cc1e0-e6f1-45dd-cd58-8853133b567d"
      },
      "cell_type": "code",
      "source": [
        "interp.plot_confusion_matrix()\n"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dx3XlLyJiQip/ERETUvmLiJiQyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFRExI5S8iYkIq\nfxERE1L5i4iYkMpfRMSEVP4iIiak8hcRMSGVv4iICan8RURMSOUvImJCKn8RERNS+YuImJDKX0TE\nhFT+IiImpPIXETEhlb+IiAmp/EVETEjlLyJiQip/ERETUvmLiJiQyl9ExIRU/iIiJuRkdICMYtPG\nDfR7vzd3Yu+QJ88zTJk+k1y5chkdy66uX73Eey9Wxy/XM7axoGKlePvTL23L88YMZufPKxm3Yrtt\n7ND2zUwc+C71QzvQ7I130zWzPTg6WOhSPS+hZXPRbPJOrt+5C0DLkJy8UDIHDhY4eDma0etPk5xq\nxcnBQq86wZTK5UWKFZYeCGPh/qsA/PBmOVKtVpJTrLbHf3XmXkPO63HbsWktH3Z5hW/X7yO7f04m\njBjA3l83YU1NpXTFanQf8BmOTk5cC7vCFx/1IvzKBaxWKy+16cQLr7xudPzHZsPq5cycMIq7iYlk\n8fbhg8FfsHLJfLb+vNq2TUJ8HFl9sjF72SYAdm7ZwMe93qJl+7d4vWtvg5KnpfIHYmNjafdqKMtW\nrKZ0SAgTxo+je9fOLF62wuhoduftF8DoxZseuO7CyWPs2bQmzdi2VUtY98M35C1cLB3SpY8RLz7L\nb+ExacaK5shMyzKBvP7NPu4kpjD4+SK0DMnJd3uuEFo2EC9XJ175ei9uLo7Maleaw1ejORFxB4B3\nvz9MeHSiEadiNwnxcUz7YjBeWbwBWPjNZC6dO830pb8A0KtDM1Yt/pYmrdrx+cAelK/2HC3ad+Za\n2BU6vlCNEuUqk69AYSNP4bEIv3qJkQPfY+bSDeQIzMOCWZMZ2rcbXy/+mW4ffGrbbuSgXuQNLgTA\nmuU/sHDudAoVLWlU7Aey67TPyZMnqVOnDnPnzrXnYf6zTRs3kDdffkqHhADQvsPrrF+3lpiYmEfs\n+fRKTU3l6+Ef0vLtPmnGc+YNZsCUBWT1zW5Qssdv1vaLfP3rxTRjtQplY8Px69xJTAHgp8Ph1CqU\n7d66gtlYfigcKxB3N4WNJyOp/fu6p9Xsr0ZS9/mWuHl4AlCibGW69R+Os4sLzi4uFC5emvOnTwDQ\npFV7GrdoA4BfjkAC8+Tj8vkzhmV/nJycnPlkzDRyBOYBoGzl6lw4eyrNNmdOHmP/rl956fdXO8/k\nL8jEuT/ik90v3fP+E7uVf1xcHIMHD6ZSpUr2OsRjc+rUSfLnD7Ite3p64uvry5nTpw1MlT7iY2MY\n/V5Her1UkxHvtOHKuXt/kX9eNJfcwYUpUDwkzfb5ihTHydnFiKh2czTs/h/yub3duHIrwbZ85VYC\neXzc763zcePKrXjbuqt/WQfQtUY+ZrcPYVqbUlQJ8rFj8vRx9uQx9m7fTIv2XWxjRUqEkCd/AQBS\nkpPZ++tmipS893eler0mth8SR/fv5sb1CIqXqZD+we0gm18AFarWAiA5OZmfFn1H9TqN0mwzY9xI\n2nTqjpPTvYmVwsVK4uyS8f7N2K38XVxcmDZtGn5+Geun3YPEx8Xh6uqaZszVzY3Y2FiDEqUPV3dP\nKjd4kXa9P2bUwg0Ur1CN0e915EZEGKu/m0Fot75GRzRMJidH7qak2pbvJqfi6uz4wHWJyam4Od/7\np/Tz8essPhBG+9n7GL/xLIMaFSIwa9q/W08Sq9XKmI97063/cJycnR+4/stP+5A9ICc1G7xoG4+4\neplX6oTQr3Mo3fqPIKvP0/XKaMGsyTSqWJADe7bT9f2PbeOXzp/lyIHd1G/awrhw/5Ldyt/Jyem+\nQs2o3N09SEhISDMWHxeHp6enQYnSR+as3nT4YAjZc+bGwcGBRm06cftGJLNHDqTZGz3w9MpqdETD\nJCSl4OL45z+PTM4OxN9Neei6uN/XTd5ynv2XbgNw6Eo0+y/dpnxe73RM/nit+H42zwQVpHiZivet\nS0lOZkTfrlwPv8on42bh6OhoW+efMxffrt/HlEUbmPHlEHZsXpeese2u9WudWbP7DKGvdebNVvVJ\nSLj3SnD9ysXUqNfkgT8oMxrd6gkUKlyYM2f+nOK5ffs2UVFRBBcoYGAq+7sTfYtrV9LOdaempnBw\n+ybmfTmYLvVCGNC2CTcirtKlXghJd5+uNzH/yYWbceTy/vPiJXdWN87fiPt9XTy5vN3+XOd9b52z\no4V8vu5pHsfRwZLmzp8nzbafV/PrhtU0r/Yszas9y/XwK3RpWZf9O7fw+aCeJCYmMGTCXDK53ns+\n7t5NZOXCuaSk3PthmCPXM1SsUZc92zYZeBaPz7nTJ9j1+7lYLBbqNW1B7J0YLp691x/bNqylco26\nBib891T+QI2atbh08QLbtm4FYPzYMTRs3AQPDw+Dk9nX2aMHGdo5lOioGwBsWPIt2QICmbnlBJPW\n7mPS2n0MmbMCX/+cTFq7D2eXTAYnTj8bTkRSp3B2vN2dcbRAyzI5WX/8OgAbT1yneemcOFjA18OZ\n5wplZ8OJSFydHJn8SkmK5sgMQP5s7hQP9GLPxSgjT+U/GTF1Pou3HWfRlmMs2nKM7AGBTPphHTG3\nb3PhzAkGjJqS5irXxSUT86Z+ydplCwCIj73DgV3bCCr0rFGn8FjduhnJp326cD0iDICDe3eQnJRM\nYO57t0ufPnGUvMEFjYz4r+lWT8DNzY1v5s2nZ/euxMbFEhQUzNQZs4yOZXclKtWgTst2fPx6MywW\nB3z8AugxagoOf3n5/ndTPunFyYN7uRV5DSdnZ7auXEK91q9Rv/Vr6Rf8MfJ2d+ar1iVsy+NblyAl\n1cq7Pxzmu91XmBhaAiwW9lyIYumBe/fyf7/vKnl83Pn29bKkpFqZtf0ip6/fe39o0I/H6VM3GBcn\nBxKSUhn80wnCbj99r5hWfD+biCuX6PhCNdtY0dLleX/oOD4dN4txQ/oyf/o4UlJSqFyrPvWbvWxg\n2sendPkqvNblPbq1a4bVmoqziwtDxk7HI7MXt29FkRAfh282/zT7DOn7Dof37SLyWjjOzi6sWfY9\nLdq8Qct2nQw6i3ssVqvVLq9Jjxw5wmeffcaVK1dwcnLC39+f8ePHkzXrg+eRE5LtkeLJteTQZaMj\nZDgTNpwzOkKGM7jJ03FF/bi4Oz/8wsWsKgQ9uHPtduVfrFgx5syZY6+HFxGR/0Bz/iIiJqTyFxEx\nIZW/iIgJqfxFRExI5S8iYkIqfxERE1L5i4iYkMpfRMSEVP4iIiak8hcRMSGVv4iICan8RURMSOUv\nImJCKn8RERNS+YuImJDKX0TEhFT+IiImpPIXETEhlb+IiAmp/EVETEjlLyJiQip/ERETUvmLiJiQ\nyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFREzIyegA8mA5PNyMjpDh7J23wOgIGc7OkJ5GR8hQEpNT\njY6Q4VQIyvrAcV35i4iYkMpfRMSEVP4iIiak8hcRMSGVv4iICan8RURMSOUvImJCKn8RERNS+YuI\nmJDKX0TEhFT+IiImpPIXETEhlb+IiAmp/EVETEjlLyJiQip/ERETUvmLiJiQyl9ExIRU/iIiJqTy\nFxExIZW/iIgJOT1sRWpq6j/u6OCgnxsiIk+qh5b/s88+i8ViAcBqtQJgsViwWq1YLBZ+++239Eko\nIiKP3UPL//jx4w/d6fz58/bIIiIi6eSh5f+HlJQUtm7dSlRUFAB3795l8uTJbNiwwe7hRETEPh5Z\n/n369OH27ducOHGCkJAQDh48SLdu3dIjm4iI2Mkj37UNDw9nxowZ5MuXj3HjxvHtt99y+PDh9Mgm\nIiJ28q9v2UlOTiYxMZHAwEBOnz5tz0wiImJnjyz/ihUrMm3aNOrUqUOzZs3o1KnTI28DfRJt2riB\nSuVCKP5sQRo3qMvly5eNjpSutm9aS63C2Qi/fJGU5GTGDe5Lu4YVaVu/PF981IuU5GQArkeE8WHn\nV2jfqBLtGlZk2XdfG5z88XBycmDEe82I3/8VgX5ZAWjTtALhv4ziwOIBtl+dW1dPs5/FYuGXb3oz\n9ZM29z1m8YKBRO8aS7UyBdLlHOwlOjKCGX3a89krNRj7RmPOHdoFwPnDe/iyY0NGtanFtF5tiI6M\nACDm5nW+GfgWo9vXZUyH+mz+boqR8e0i+kYE3/R9jTHtajGxc1POH95NSkoya6YMZ3zH+oxpW5Nt\nP0xPs8/pvVsZ2aoim7+daFDqtB4559+9e3dSUlJwdHSkdOnS3LhxgypVqqRHtnQTGxtLu1dDWbZi\nNaVDQpgwfhzdu3Zm8bIVRkdLFwnxcUwbPRivLN4ALJw9mUvnTjNj2S8AvPdaM1Yt/pYmrdrxxaD3\nKFSsFMMmf0tkRBgdmlaldIVq5Mn/ZBfcD2PeYu/RC/eNL994kE4fzX3ofp1aVsPPNzPHz4WnGbdY\nLIz7sDURN6Ife9b09sNn71OwfHU6tpzNmf3b2b50DjmCivDt4O60+XgCeZ4tzebvpnBww49Ua/UG\nP00eTvZc+Wk3eAoJsTF81aUZgQWLEVzm6emNpaM+ILhcddo1f51zB3awa9lcrp8/xeUTB+k8aTkp\nSXeZ/m5LchUuyTPFy3Fow4/s/nEeOYKLGh3d5pFX/gsXLmTJkiUsXLiQs2fPcvv2bVauXPmvHnzk\nyJG0bt2a5s2bs3bt2v8c1l42bdxA3nz5KR0SAkD7Dq+zft1aYmJiDE6WPmZ9NZK6L7TEzcMTgBLl\nKtNtwHCcXVxwdnGhcPHSnD99AoCmrdvTvN1bAGTzz0GOXHm4ePaUYdkflxHTVjNk8r/7e/2HgGxe\ndAmtzvi5G+9b92aLqhw8cZmzlyMfV0RD3Lp2lSunjlC5WTsAgkpX4pVB4zn263pyBhclz7OlAajx\n8ltUa/UGABFnTxAUUgkAV4/M5CpUnPDzJ405ATu4fS2Mq6eOUuGFtgDkK1WRVgPGcmb/NorXaoqz\nSyZcPTJTql5zjm2913vZcuen/chv8PTJZmT0NB555b93717b7+/evcuhQ4cICQmhRYsW/7jfjh07\nOHXqFAsWLCAqKopmzZpRr169/57YDk6dOkn+/EG2ZU9PT3x9fTlz+jSlSpc2MJn9nT1xjL2/bmbS\n92tZ9u1MAIqUCLGtT0lOZu+vm3n1rR4AVK7dwLYu4uplLp0/S4FnS6RvaDvYeejcA8dLFMrFmmnv\nkiN7FrbtP80HoxcTfScBgFG9mzNs6ipcnNP+M/L3zUzXV2pSve3nfD+mk92z21PYmeN4B+Ri9bRR\nHN+xkcw+2Wjy9gDCzxzHI4s3cwZ1IeL8KQILFOX57h/hkcWHoJBKHN60kqCQysRG3eDS8YPUCH2y\nn4e/Cj977zlZP+NzTu7ahKd3Nhp07o8FC9bUFNt2Lm7u3Lx679VkzgIZ54r/D4+88h8+fLjt1+jR\no1m+fDmJiYmPfOBy5coxduxYALy8vIiPjyclJeURexkjPi4OV1fXNGOubm7ExsYalCh9WK1Wvvi4\nN90HDMfJ2fmB68d80ofsATmp2fDFNOvuRN/mo+6v8WqnHvjnzJVekdPV6YvXWLHpEM3fnUyF0OF4\nebgysldzAOpWLkJWL3e+X733vv1G9WnB8KmruH0nPr0jP3YJd6KJOHeSfCXK0Wv2OkrVeYG5H3cl\n/k40p/ZupeFbH9Dz61U4OruwYsIQAOq0e5fLJw4zuFlZPnu5OsWrNyBHUBGDz+TxSYiNJuL8SZ4p\nXo5uM9ZQovbzLPj0HfKWrMC+1QuJvxNNXHQUh35eRvLdR3elUf7nL+hxc3Pj4sWLj9zO0dERd3d3\n4N7UUfXq1XF0dPzfE6YDd3cPEhIS0ozFx8Xh6elpUKL08eOC2eQNLkjxMhXvW5eSnMzwvl25Hn6V\nT8fPSvNnd/N6BD3bv0iF6nVo07lnekZOVzsOnmPI5JXciUskPiGJUV+vpWH1YrhmcmZ4z2a8O3zB\nffvUqVQEnywezF+1x4DEj5+rR2Y8vX15tkpdAMo1ak1czC3iom8RVLoS2QLz4ujkTJWX2nNq71YA\nFo76gKLV6/PRsv30X7STM/t3cGjTT0aexmPl6pEZz6y+FK5cB4CQhq2Ij7nFM8XLERRShenvtmTB\n4G7kL10FV08vg9M+3COnfV555RXbd/wAREREULBgwX99gPXr17Nw4UK+/jrj3hVSqHBhFv7w5z/k\n27dvExUVRXCBJ/tNzEf5dcNqThw5wK8bnwXg9s1IOresy0djprN22ffcTUhg6MS5aV4VxN6Joc8b\nrWjQLJSWr3UxKnq6yOWflYRPol4cAAAgAElEQVS7yURG3QHAydGR5OQUQorkJtA/Kz9//R4Abpmc\ncXF2JJu3J5cjblGyUC7OrRsGgE8Wd+aPfoM+ny/i2xW7DDuX/6+s/oEkxsWSmpqKg4MDFosFi8WB\n/CUrcGb/r7btHBwdcXC4d4Fwas9WGrz5PhaLBXevrBQoW5VzB3dRomZjo07jscril5PE+L89Jw4O\nODm7UO/ND6j35gcAbJr7Ff75/n1XprdHln+PHj1sv7dYLHh6elKkyL97CbdlyxYmT57M9OnTyZw5\n8/8/pZ3VqFmLzm++zratW6lStSrjx46hYeMmeHh4GB3NrkZMnZ9mObR2ab78Zhknjx3i/JkTjJ/3\n033TQTO+HEZIxapPffEDvNmyGkXyB/DK+zNITbXSJbQGq7Yc5dcDZ8lR/X3bdm2aVqB62QK2u4K6\nD/3zeV0z7V2GTF7Jlr1P5pviAfkL4eXrx56V31O+SSiHN6/ELXMWilatx7rZXxJ+9gQB+Quxa8V8\ngkIqA5Atdz5+2/4z1Vp2JCkxgTP7t1O67ouPONKTwz9fITL7+rFv9feUbRTK0V9W4erpxeXjB9g4\nZxzN+37BnajrHFi3mLbDZhod96EeWf6LFy9mxIgRacY6duzIjBkz/nG/mJgYRo4cyaxZs8iaNet/\nS2lnbm5ufDNvPj27dyU2LpagoGCmzphldCzD/LhgNhFXLvH689VsY0VLl+eDYeNYsWA2vn4B7Pzl\nZ9u6Fu3f4vnQDkZEfSz8fDKzdvq7tuU1094lOSWFRm+N5+N3mrJ/0QBSU63sOHiOD79cYmDS9Gex\nWHjlo69YOPIDNs2fgmdWH14dNJ6s/jlp0ecz5nzUBYvFgn/egjR7796cf8sPRrF8/Mfs/PE7wErB\nctUp17i1sSfyGFksFloNGM/Szz9g64KpeGT1pdWAcWTLnZ/j239m3Gt1cHB0pM7rvfENfAaApaP7\ncenYPu7cvI6jszOHfl5G+efb2O4YMuQ8rH98X/PfLF++nPnz53Pq1Kk00zxJSUlERkY+8ovdFixY\nwPjx48mXL59t7LPPPiNnzpwP3D4h+f8T/+m148wNoyNkOA1DPzI6Qobz0ein9z2X/4/E5KfvA6j/\n1Uf1Hjx9/dAr/+eff54KFSrQu3fvNF/k5uDgQHBw8CMP2Lp1a1q3fnp+2ouIPE3+8W4ff39/Jk+e\nTGRkJOXLl6d8+fKcOnUK5wfcFigiIk+OR97q2bdvXyIj//yUYkJCAu+///4/7CEiIhndI8v/1q1b\ntGvXzrbcoUMHoqOf/O8rERExs0eWf1JSEmfOnLEtHz58mKSkJLuGEhER+3rkrZ79+vXj7bffJiYm\nhtTUVLy9vRk5cmR6ZBMRETt5ZPmXLFmSNWvWEBYWxs6dO1myZAldunRh69at6ZFPRETs4JHlf+DA\nARYvXszKlStJTU1l8ODBGfbbOUVE5N956Jz/tGnTaNSoET179sTHx4dFixaRJ08eGjdurFs9RUSe\ncA+98v/yyy8JDg5m0KBBVKx471sf//oFbyIi8uR6aPlv2rSJJUuW8NFHH5GamkqzZs10l4+IyFPi\nodM+2bNnp1OnTqxZs4Zhw4Zx8eJFrly5QufOndm8eXN6ZhQRkcfsX/1nLuXKlWPEiBFs2bKFmjVr\nMmHCBHvnEhERO/qf/icvT09PQkND+f777+2VR0RE0sH//N84iojIk0/lLyJiQip/ERETUvmLiJiQ\nyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFRExI5S8iYkIqfxERE1L5i4iYkMpfRMSEVP4iIiak8hcR\nMSGVv4iICT30//AVY2XzyGR0hAynQNMXjY6Q4Ry+GmN0hAzFz8vV6AhPDF35i4iYkMpfRMSEVP4i\nIiak8hcRMSGVv4iICan8RURMSOUvImJCKn8RERNS+YuImJDKX0TEhFT+IiImpPIXETEhlb+IiAmp\n/EVETEjlLyJiQip/ERETUvmLiJiQyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFRExI5S8iYkIqfxER\nE1L5i4iYkMpfRMSEVP4iIiak8hcRMSEnowNkFJs2bqDf+725E3uHPHmeYcr0meTKlcvoWHa3ce1P\nTBg9lLt3E8nq7cPAYWP5cdG3bFy30rZNQnw83r7Z+H7lFs6fPcXgfj24FhGGk5Mzr73VnRdavmrg\nGTweTg4W3q0bTPsqz1D38y1ERCcC0KZSblqUzYWDBfZduMWQFcdJTrHi6+nCoKZFyJfdnZRUK8sP\nhDFz6wXb471W5Rm61QnijZl72X/xtlGn9VhUz+9Dk2f9wAI345KYtesy4TGJtvXvVsuLp6sTQ9ed\nBiDI15125QJxd3YkMTmVHw6GcfBqjFHxH7tT29bw67yxacairpwj5IXXOLZhKW5e3rbxKm17Elyp\nLjHXw/h50kfcjrgCViulmrShZKNX0jt6Gip/IDY2lnavhrJsxWpKh4QwYfw4unftzOJlK4yOZlcR\nYVfp/15n5ixeR1DBwsyfPY1P+3VnzpL1vNd/iG27IR/2JF+BQgAM7NWFxi+2JrT9m1yPCOeluhUo\nWaY8efMXMOo0Houxr5Tk6JXoNGMlcnnxasU8tJq0k5iEZEa3Ls6rFXMze9tFetcvwPkbsbz73UE8\nMjny3VvlOXY1hp1nbzKgaWEcLRZuxt416GwenxxemXglJCf9fjpBVHwSzxXwpVOl3Hy69l7Rlwr0\nIp+vO9f/cq49qudl+s5LHLwaQ64srgyqX4B3lxwlPinVqNN4rApUqU+BKvVtyye3ruLk1lU4u3lQ\nsvGrVHr5nfv2WffVQPKWqUbI8+2JuR7GnHdfIFexcvjmMe7fjd2mfeLj43n33Xdp06YNLVu2ZOPG\njfY61H+2aeMG8ubLT+mQEADad3id9evWEhPz9FytPIiTszMjx39NUMHCAJQuX4kzJ4+n2ebU8WPs\n2bmV1m3fsC1XqFoDgOz+ATyTP/i+fZ5EUzafY+LGs2nG6hb1Z/WRCGISkgFYsu8q9Yr6A1DA35Od\nZ28CEJuYwrGrMRTw9wBg+YEwPln+G8kp1nQ8A/sIzOJKeEwiUfFJABwNv0OurG4AuDhaeKV0ThYf\nCrdt7+HiiI+HC0fD7wBw+XYCd5NT8fPMlP7h00Hy3UR+nTeWqu17/+N2JRq0oljdFgBkzp6DrAF5\niLpyPh0SPpzdyn/jxo0UK1aMuXPn8uWXXzJixAh7Heo/O3XqJPnzB9mWPT098fX15czp0wamsj/f\nbNmpWquubXnrxrUUL1U2zTaTvhxOh849cHK69yKxQpUarF6+iNTUVM6dOcnVSxcpGVI+XXPbw6FL\n90/N5M3mzuWbcbblyzfjyZvtXsHvPBtFvaL+ODpYyJ7ZhWKBXuw6G/XQx3pSnY6MxS+zC7myuAJQ\nPk8WjoTduyh6qUQAW8/dTHPVH3s3hXM346ic997UR8HsHqRYrVy5nZD+4dPB0XULyVk4hKw58gBw\n6eB2Frz/MrO7NOSXrz8jOenecxNcqR4ubvf+7lw9vp/YqOvkfLaMYbnBjtM+jRo1sv0+LCwMf39/\nex3qP4uPi8PV1TXNmKubG7GxsQYlSn87tm5izvQJzJj/k23s4rkzHNq3m8/Gf20b++Djz2jfvD5z\nZ0ziTsxt+n36Odn8Mu6f7X/h+vuc9R8SklNwc753vTRp4xlmdSzLL31r4ObswOxfL3Iy4o5RUe3m\nVnwy3x8IY1jjQiQkpZCYnMrgdafJndWVEjm8GLjqBAX9PNPsM33HJfo9F8SrZXLi4uTAV1vOk5z6\n5L8K+jtraip7l83ihQETAfDL/ywuv0/9JCfEsXzYO+xZNI2KoV0BiL5+lYUftiMxNoa63QbjnsXH\nyPj2n/MPDQ0lPDycyZMn2/tQ/2/u7h4kJKS9MomPi8PT0/Mhezxdfl79I8MH9WHCzB9sU0AAq39c\nzHMNmuLs7Gwb69HpVbr26s+LrdoQHnaF11o0oHCxEpQqU8GI6HYVfzeFTE5/vjh2dXYk7m4KAJ82\nK8r6Y9eYvOkcXm5OTGpbmnpF/Vh79JpRce3iGW83XijmT8+lx7gRl0SVfN70qpmPO3dTmL37Mn+f\n2XJ2tNCzRj7GbTnP0fA7BGbJRP86wVyIOklkbJIxJ2EnYScO4OLqbpu3D6pQ27bOydmFkOfbsXvR\ndFv5e2XPyevT1nM74jJLP+mEo3Mm8pWtYUh2SIdbPefPn8+kSZPo06cPVmvG/OlfqHBhzpz5c4rn\n9u3bREVFEVzgyX4T89/YvmUjn338AVPnLaVoyZA06zb/vIpqterZlqNuRvLb4QM0btYagIAcgZQq\nU4H9u7ena+b0ci4yltw+7rblZ3zdOXv93qvBykE+rPx9rjs6PpntZ25SNq/3Ax/nSVY0wJNT12O5\nEXevuHecjyJXVjfy+7jTvXpeJjQvSo/qeSmYzZ3hjQuRK4srDhZsc/5XbicSHpNIfl/3fzrME+ns\n7k3kLVPdtnwr7AKJcX+++ktNScHB0YnkpLscWbeQ1JR7Fw5Z/HORr2wNLhzYlu6Z/8pu5X/kyBHC\nwsIAKFKkCCkpKdy8edNeh/tPatSsxaWLF9i2dSsA48eOoWHjJnh4eBiczL7i4+MY2KsLY6bOI3+B\nwvetP/XbUfL/fpcPQJasPvj4ZmPz+nu3gd6+FcWBvTsJLvRsumVOT2uORNCweAA+Hi44Olh4tWJu\nVh2OAOD8jThqFM4OQCYnB8rn8+b0tadv2icsOpEC2T3wdHEE7t3dExWfRIf5h+i66ChdFx3ly1/O\nczIyjn4/nSAy9i7uLo7k9733prCvuzO5srhy5XbiPx3miRR5/jg+uf98r3D7t+P5dc4YrFYryXcT\nObzme/KVrYGTswu7F07lt43LALgbH8vlI7vInrfQwx46Xdht2mfPnj1cuXKF/v37ExkZSVxcHN7e\nGfPKyM3NjW/mzadn967ExsUSFBTM1BmzjI5ldxvX/ETUzUj6du+YZnzmD6txdnIiPj6ObNn/nM93\ncHBg9OQ5jB4ygLEjPsZqtfJCy1fTvDp4Evl4uDDz9T/ffJvRoQwpqVbenLWP2dsuMKtjGSwWCzvO\n3OD73ZcBGLD4KP0aF6Zl2UAsFgvbTt1g0d6rACzuWhFHBwt+XpkY3qIYiUmp9F98lCN/u5X0SbD/\nSjT5fN35uEEBrEB8Uirjfjn/0O1jElOYtO0ib1bMg5OjBasVvtt/9al8w/dOZATuWbPZlmt07Mf6\niR8xu0sDLA6O5C1TnZAXOwDQpO84Nk0dwp7F00lNSSF/+Vo8W7uZUdEBsFjtNBeTkJBA//79CQsL\nIyEhgXfeeYfatWs/fPtke6R4cp0Of/quIv+rV6buMDpChlO8YLZHb2Qifl6uj97IZMY8f/+rerDj\nlb+rqyujR4+218OLiMh/oO/2ERExIZW/iIgJqfxFRExI5S8iYkIqfxERE1L5i4iYkMpfRMSEVP4i\nIiak8hcRMSGVv4iICan8RURMSOUvImJCKn8RERNS+YuImJDKX0TEhFT+IiImpPIXETEhlb+IiAmp\n/EVETEjlLyJiQip/ERETUvmLiJiQyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFRExI5S8iYkJORgeQ\nB/P2cDY6QoZTurCf0REynPqFfYyOkKH0mPCr0REynDHPF37guK78RURMSOUvImJCKn8RERNS+YuI\nmJDKX0TEhFT+IiImpPIXETEhlb+IiAmp/EVETEjlLyJiQip/ERETUvmLiJiQyl9ExIRU/iIiJqTy\nFxExIZW/iIgJqfxFRExI5S8iYkIqfxERE1L5i4iYkMpfRMSEVP4iIiak8hcRMSGVv4iICan8RURM\nSOUvImJCKn8RERNS+YuImJCT0QEyik0bN9Dv/d7cib1DnjzPMGX6THLlymV0LLtLSkpi+CcDmDpx\nLLsOnyZnYNpzHjywLz8tX8yOgycBOHv6FH17vUNEeBhOTs50fqcHLV9ua0R0u6mW35uGRbJjAW7G\nJfHNnitcv3OXV0JyUjTAEweLhWMRd5iz5wpWYFijgmn2z+rmzKJD4aw/ecOQ/I/T9auX6PVidfxy\nPWMbCypWii6ffmlbnjdmMLt+XsnYFdsBuBF+lRnD+nL9yiWsViv1QztQt1X7dM/+uDk5WhjYvARd\n6heiZO8fCYuKB+C9Js/SvGIeHCwWDl+Motc3e4mJT7LtZ7HAyg+f41RYNN2/3p3mMYvmysLagXVp\n+cVmfj1xPX3PJ12PlkHFxsbS7tVQlq1YTemQECaMH0f3rp1ZvGyF0dHs7vVXW1CydJkHrjt25BCr\nVy5PM/beO2/SrOXLtO/4FhHhYdSpWpYy5SqSP7hAesS1uxxemWhdKgcDV50kKj6ZWsE+vFEhN/su\n3yaHVyYGrDoFQN/a+amW34fNZ27S76eTtv1dnRz4tGEBdl+8bdQpPHbefgF8vnjTA9ddOHmMvZvW\npBmbNrgPJavUouErb3Aj/Cp9W9elSJmK5AoqlA5p7eebd6py4PzNNGNNyuTi+XK5qDdkPXGJyUzu\nVJF3GhRi+JIjtm061Awmu5crp8Ki0+xrscDItmW4Fp2QLvn/zq7TPgkJCdSpU4fFixfb8zD/2aaN\nG8ibLz+lQ0IAaN/hddavW0tMTIzByeyvR+9+9O436L7x1NRU+vXqzvsffpxm/Pixo1StXgsA/4Ac\n5A8qwMkTv6VH1HSR0ysTEXcSiYpPBuBYxB0Cs7py4nosc/deJSXVSkqqlbM34wjMkum+/Z8v5sfW\nc1HcTkhO7+jpLjU1lZnDP6Tl233SjD/XvA21XnwZAN+AnPjnfoawC2eNiPhYfbHiGCOXHU0z9sfV\nfGxCMlYr7D59g8KBWWzr/bK40vG5YKasO/n3h+O1mkEcuXSL89fu2D37g9i1/CdNmkSWLFkevaHB\nTp06Sf78QbZlT09PfH19OXP6tIGp0keZ8hUfOD531nQKP1uUkHLl04xXqVGLZYu/JzU1lTOnTnL5\n0gVCypZ/4GM8ic7ciMPPM5Ot2MvlzsLR8BjO3ognLDoRAAcLFA3w5OyN+DT7emZypHJeb9Yej0z3\n3PYUHxvDF+91pPdLNfnsnTZcOXfv1c+GRXPJHVyY4OIhabYvV7shru4eAJw6tJdbkdcoVPrJ/zuy\n58z903gnrkZz6EKUbfm54gHsPfvndkNCS/H58qNE/2UaCMDPy5U36xRg6KLD9gv8CHab9jlz5gyn\nT5+mZs2a9jrEYxMfF4erq2uaMVc3N2JjYw1KZKxrEeFMnzye5Wt/ISY67fTFJ8NG8VKj55gxZQIx\n0bcZ/NkY/PwDDEr6+N2KT2bhwXAGNyxIQnIKicmpDF+f9qq1fblAouKS2HnxVprxugWzsf38LRKS\nU9Mzsl25untSucGLNG77Fr4BgayaN40v3uvIh5MXsPq7GXwyaxlxd+5/hRwZdoUhnVoSGxNNp0Gj\n8PL2NSB9+urRuAjZvVyZvv7eD8daxQLI6uHCkl2XaF0lb5ptB79citHLj933QyE92e3K/7PPPqNv\n3772evjHyt3dg4SEtPNu8XFxeHp6GpTIWJ/070OPPh+SNav3fevebBdK736DOHo2jB0HTzLlqzHs\n3bXDgJT2kcfblaZF/ei9/DhvLzzGDwfC6VE9L3Dvir9Tpdz4uDszbssFrNa0+1bMm5UdF27d/6BP\nsMxZvXntgyFkz5kbBwcHGrXpxO0bkXwzciDN3uiBh1fWB+6XLUcgX/74K0PnrWTBV59xYOuGdE6e\nvvq/VJzGIYG0+mIzcXdTcHV25ONWJflg7r77tq1V1B8fDxcW7bxoQNI/2eXKf+nSpZQqVYrcuXPb\n4+Efu0KFC7PwhwW25du3bxMVFUVwgafjTcz/1fq1q/h16y8MHtiXlJQUbkXdpHThZ1j7yy4OH9xP\ns5b35nNzBuaiTPmK7Nrx60Onj540Rf09ORUZy824e1dkOy/c4q3KecicyZHWpXLg7Gjhy83nSflb\n8QdkzoSrkwMXouIf8KhPrtjoW8TGROMXmMc2lpqawsHtmzh1eB/zvhxMakoKd6Jv8Xa9EMau2M62\nlUuo3rQlDo6O+AXmoVTV2hze8QulqtY28Ezsp8/zRSlfIBsvjtpE7O/v9ZR8xpuc3m782Pfe+2Ou\nzo44OzngmzkTV2/GUyyPN0e+aApAVg8XZnatzMDvDvD99gvpltsu5b9p0yYuXbrEpk2bCA8Px8XF\nhYCAACpXrmyPw/1nNWrWovObr7Nt61aqVK3K+LFjaNi4CR4eHkZHM8SJi3/OWV+6eJ6WTeux4+BJ\nUlNT8c2WnXWrf6JR0xe5dSuKvbt20KzFywamfbzCYhJ5rmA2PFwcib2bQomcXtyKT6KQnwc5s7gy\ndN3p+4of7r1i+OM9gafJmaMH+XpYPz795ke8vH3ZuORbfAMC+WzBOhwcHYF7t4MO6dTKdqvnsq/H\nY3GwUOP51iTExfLb3h3UbdXOyNOwmxLPeNOq8jPU/mSdrfgBdp6OpEC3pbbl1lXyUqVQdtutnn3m\n7LWtW9KnJqOWH306bvX88ss/7wEeP348gYGBGbb4Adzc3Phm3nx6du9KbFwsQUHBTJ0xy+hYdnf9\nWgQtmta1Lbd8vh5OTk7MX7KKHDkD79vewcGBKbO+ZfCgfowYPBCr1UrLl9tSu2799IxtVweuxJDX\nJ4pB9YKxAvFJKXy19QIvFPMnm4czQ/5yT//pyDhm7LwMgLe7M7fjn747fEpUqkGdlu345PVmWCwO\n+PgF0GPUFFvxP0iPz6cxe+RAfpw1idSUZEJq1KV601bpmPrxy+6ViaXv17ItL+1Tk+RUKztOReLl\n7szq/s/Z1l2+EUfrMb8YEfN/YrFa/z5z+Xj9Uf4vvfTSP25ngjvj/ic3Yp6+q8j/6sNVJ4yOkOHU\nL+xjdIQMpceEX42OkOFcm/HgH7x2/5BXt27d7H0IERH5H+m7fURETEjlLyJiQip/ERETUvmLiJiQ\nyl9ExIRU/iIiJqTyFxExIZW/iIgJqfxFRExI5S8iYkIqfxERE1L5i4iYkMpfRMSEVP4iIiak8hcR\nMSGVv4iICan8RURMSOUvImJCKn8RERNS+YuImJDKX0TEhFT+IiImpPIXETEhlb+IiAmp/EVETEjl\nLyJiQip/ERETUvmLiJiQyl9ExIQsVqvVanQIERFJX7ryFxExIZW/iIgJqfxFRExI5S8iYkIqfxER\nE1L5i4iYkMpfRMSETF/+sbGxXLhwgQsXLhAXF2d0nAwrOjra6AiGetDHYcLDww1IkvHcvHnT6AgZ\nyvbt242O8K+Y9kNehw8fZujQoURHR+Pt7Y3VauXatWv4+/szaNAgChUqZHTEDKVdu3Z88803RsdI\nd+vWrWPYsGHEx8dTo0YNBg4ciKenJ2DO52TTpk0MHz6cHDly8OGHH9K7d29SUlKIj4/no48+okaN\nGkZHTFdLly5Ns2y1Wpk0aRJvv/02AC+++KIRsf4VJ6MDGGXYsGEMHTqUoKCgNONHjx7l008/Zd68\neQYlM84/nXNEREQ6Jsk4pk6dypIlS/Dy8uKHH36gY8eOTJ8+ncyZMz/w1cDTbtKkScycOZOrV6/S\nuXNnJk6cSOHChYmMjKRz586mK/8JEyaQNWvWNOedmJjI5cuXDUz175i2/K1W633FD1C0aFFSUlIM\nSGS8WbNmUalSJfz8/O5bl5ycbEAi4zk6OpI1a1YAWrduja+vLx07dmTy5MlYLBaD06U/FxcXcubM\nSc6cOfHz86Nw4cIAZMuWjUyZMhmcLv2tWLGCiRMncuLECfr27UtgYCBbtmzhnXfeMTraI5m2/EuW\nLEnnzp2pU6cOPj4+AERGRrJmzRrKly9vcDpjTJgwgSFDhjBgwABcXFzSrNu5c6dBqYwVEhLCW2+9\nxdixY3F1daVOnTpkypSJ1157jVu3bhkdL935+voyY8YMOnbsyPz584F77318/fXXBAQEGJwu/WXK\nlImePXty9uxZPv30U0qXLk1qaqrRsf4V0875A+zevZvt27cTGRkJgJ+fH1WqVKF06dIGJzNOfHw8\nmTJlwsEh7b0AR48epWjRogalMtbOnTspX758miv9O3fusHLlSlq1amVgsvSXkJDAhg0baNSokW3s\n6NGj7N69m5dfftmUV/9/tXTpUjZv3syYMWOMjvJIpi5/ERGzMv2tniIiZqTyFxExIZW/PLUuX75M\nsWLFaNu2LW3btiU0NJRevXr9vz+w9sMPP9C3b18Aevbs+Y+3v+7bt49Lly7968dOTk7WZ0skXan8\n5anm4+PDnDlzmDNnDvPnz8fPz49Jkyb958cdM2YM/v7+D12/ePHi/6n8RdKbaW/1FHMqV64cCxYs\noHbt2jRs2JBLly4xbtw4Vq5cydy5c7Farfj4+DBkyBC8vb2ZN28e3333HQEBAWk+/1C7dm1mzpxJ\n7ty5GTJkCEeOHAGgQ4cOODk5sXr1ag4dOkS/fv145pln+OSTT4iPjycuLo733nuPypUrc/bsWfr0\n6YObmxsVKlQw6ikRk1L5i2mkpKSwbt06ypQpw6lTp8ibNy99+vQhLCyMyZMns3DhQlxcXJg9ezZT\npkyha9eujBs3jtWrV+Pt7U2XLl3IkiVLmsdcvnw5kZGRfP/990RHR9O7d28mTZpEkSJF6NKlC5Uq\nVaJTp068/vrrVKxYkevXr9O6dWvWrl3LhAkTaN68Oa+88gpr16416FkRs1L5y1Pt5s2btG3bFoDU\n1FTKli3La6+9xvz5822f59i/fz/Xr1+nY8eOANy9e5dcuXJx4cIFAgMD8fb2BqBChQocP348zeMf\nOnTIdtXu5eXF1KlT78uwc+dOYmNjmTBhAgBOTk7cuHGDkydP0qlTJwAqVqxoh7MXeTiVvzzV/pjz\nfxBnZ2fg3lcWlChRgilTpqRZf/jw4TQf7HrQJzctFssjP9Hp4uLC+PHjbZ8k/4PVarV9mM6sXyki\nxtEbvmJ6xYsX59ChQ0A6uNUAAAENSURBVFy/fh2AVatWsX79evLkycPly5eJjo7GarU+8Kt6S5cu\nzZYtW4B7n/pt2bIld+/exWKxkJSUBECZMmVYtWoVcO+VyNChQwEICgriwIED8H/t3LEJhEAQRuE/\nENHI0GCrMLYCOxCMjFQsQHBB4y1owSqsRwThYhs4uJv35RvOY5lg9DtngPE/+PnDvLIs5b3XMAzK\n81xZlimEoKIoNI6juq6Tc07OOV3X9XrbNI3O81TbtnqeR33fK01T1XWtfd+1rqu899q2TTFG3fet\naZokSfM8a1kWHcehqqqUJIwjvofzDgBgEGsfADCI+AOAQcQfAAwi/gBgEPEHAIOIPwAYRPwBwCDi\nDwAGfQBhsGfImhpeuwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "19127cd9fa02a15775d6009afa3ecfb416ded585",
        "id": "ediA8ZmYP8oG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2da393da-420b-48e1-9976-a486eb402144"
      },
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv(path_train/'train.csv')\n",
        "df_train.shape"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14993, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "bf7b5862cb664497355aa1c28520fc036deb4eda",
        "id": "DxzsisdoP8oN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d1f0bdfc-3691-4bdf-bdb2-a5325076ea50"
      },
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv(path_test/'test.csv')\n",
        "df_test.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3948, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "91df9b9288ef7d503dc0ebda0aac066fec14966c",
        "id": "KtPHgKrFP8oQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Using Fastai Tabular**"
      ]
    },
    {
      "metadata": {
        "id": "cUBGwBRzhcIy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7842f1a0-e646-4ea5-8bcc-69f5c531ebb5"
      },
      "cell_type": "code",
      "source": [
        "os.listdir(path_train_sentiment)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2d6e98b372cfec743ef877a7050e326e1d59f022",
        "id": "FMnbMorIP8oS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "69ffd046-0ecc-490f-bc65-fe04f57cb8cb"
      },
      "cell_type": "code",
      "source": [
        "import json\n",
        "from pprint import pprint\n",
        "\n",
        "tuplist = []\n",
        "for filename in os.listdir(path_train_sentiment):\n",
        "    if filename.endswith(\".json\"):\n",
        "      with open(path_train_sentiment/filename) as f:\n",
        "        data = json.load(f)\n",
        "        tuplist.append( (filename[:-5], data['documentSentiment']['magnitude'], data['documentSentiment']['score']) )\n",
        "\n",
        "tuplist[0]"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('e6ff63097', 1.2, 0.3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7824ccdd224bef71a5871761c501b892633c9721",
        "id": "W3BROlj_P8oX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f1e77980-619d-41e2-f22f-88f946f5a3ac"
      },
      "cell_type": "code",
      "source": [
        "df_sent = pd.DataFrame(tuplist, columns=['PetID', 'magnitude', 'score'])\n",
        "df_sent.head()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PetID</th>\n",
              "      <th>magnitude</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>e6ff63097</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14831f659</td>\n",
              "      <td>0.4</td>\n",
              "      <td>-0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>065906a54</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>03aae5768</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e4dd90768</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PetID  magnitude  score\n",
              "0  e6ff63097        1.2    0.3\n",
              "1  14831f659        0.4   -0.4\n",
              "2  065906a54        1.7    0.2\n",
              "3  03aae5768        1.1    0.5\n",
              "4  e4dd90768        2.9    0.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5a1176f6114ac72d0ae9b04b082581d76c2de04d",
        "id": "u2NTK4-RP8oZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_train_sent = pd.merge(df_train, df_sent, how='left', on='PetID')\n",
        "df_train_sent.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3d58e7bae02480322e29b68c109265cc3a7dd031",
        "id": "9vZwOEvJP8of",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_train_sent.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3900c7246334835a067ed4f5bfa10d53e23972e6",
        "id": "NJMxa9HLP8oi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Add in Description Length Column\n",
        "alist = []\n",
        "for i in range(len(df_train_sent)):\n",
        "    alist.append(len(str(df_train_sent.iloc[i]['Description'])))\n",
        "# Create a column from the list\n",
        "df_train_sent['desc_len'] = alist\n",
        "df_train_sent.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d9734cd899445248994bc91ed27d2de062ff2a98",
        "id": "NR8gIYB3P8om",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pprint as pp\n",
        "with open(path/'train_metadata'/'000fb9572-6.json') as f:\n",
        "    data = json.load(f)\n",
        "    x = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['x']\n",
        "    y = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['y']\n",
        "    string = '000fb9572-6.json'\n",
        "    a = string.find('-')\n",
        "    print(string[:string.find('-')])\n",
        "#     pp.pprint(data)\n",
        "#     pp.pprint(x)\n",
        "#     pp.pprint(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "fe19c183ff1bef96b57d97c31d40860ecf669363",
        "id": "-k4SUFEcP8op",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get train image metadata\n",
        "path_train_img_meta = path/'train_metadata'\n",
        "\n",
        "tuplist = []\n",
        "for filename in os.listdir(path_train_img_meta):\n",
        "    if filename.endswith(\".json\"):\n",
        "      with open(path_train_img_meta/filename) as f:\n",
        "        data = json.load(f)\n",
        "        x = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['x']\n",
        "        y = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['y']        \n",
        "        tuplist.append( (filename[:filename.find('-')], x, y) )\n",
        "\n",
        "tuplist[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e8582732c9b4e725daaec1fe0b3b06f1035a03d8",
        "id": "o6SJyZgbP8oy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_meta = pd.DataFrame(tuplist, columns=['PetID', 'vert_x', 'vert_y'])\n",
        "df_meta.head().transpose()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "831c8307b1b0255b4a5d26833ff0919dadda03d5",
        "id": "wqSy_OxzP8o0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# aggregate training data\n",
        "grouped1 = df_meta.groupby('PetID',as_index=False)['vert_x', 'vert_y'].agg({'vert_min':'min', 'vert_max':'max', \n",
        "                                                                               'vert_mean':'mean'})\n",
        "\n",
        "grouped1.head().transpose()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "33cd4ffaeb6fa74a5f43acd335871781bc1adada",
        "id": "qerCl43hP8o3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "grouped1.columns = ['PetID','PetID1','vert_x_min','vert_y_min','PetID2','vert_x_max','vert_y_max','PetID3','vert_x_mean','vert_y_mean']\n",
        "grouped1['vert_x_mean'] = grouped1['vert_x_mean'].apply(lambda x: int(round(x)))\n",
        "grouped1['vert_y_mean'] = grouped1['vert_y_mean'].apply(lambda x: int(round(x)))\n",
        "grouped1 = grouped1[['PetID','vert_x_mean','vert_y_mean']]\n",
        "grouped1.head().transpose()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ed9a82285d39c7bc366f35e0791e401a8d4c4113",
        "id": "C0GvQajrP8o6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "grouped1.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "324976c3cda5f180adf0d345c550b6c97d2fd0ed",
        "id": "I2yzhSO6P8o9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_train_meta = pd.merge(df_train_sent, grouped1, how='left', on='PetID')\n",
        "df_train_meta.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cc84ce66548c6aa0235247e4c6a0d9465571096f",
        "id": "OgJNxKiUP8pA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_train_meta.head().transpose()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e383aa6b3b4de0475ad647954112fa6d4e067be4",
        "id": "V4hhCpyLP8pD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# for test data\n",
        "import json\n",
        "from pprint import pprint\n",
        "\n",
        "tuplist = []\n",
        "for filename in os.listdir(path_test_sentiment):\n",
        "    if filename.endswith(\".json\"):\n",
        "      with open(path_test_sentiment/filename) as f:\n",
        "        data = json.load(f)\n",
        "        tuplist.append( (filename[:-5], data['documentSentiment']['magnitude'], data['documentSentiment']['score']) )\n",
        "\n",
        "tuplist[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "46b917ba590b589f7062f84a03ec7f3068d0c4b7",
        "id": "HLLJL_v8P8pE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_sent = pd.DataFrame(tuplist, columns=['PetID', 'magnitude', 'score'])\n",
        "df_sent.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cd33a491a895117fd8a44868661ff162accf36fa",
        "id": "oUX16ZkQP8pL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_test_sent = pd.merge(df_test, df_sent, how='left', on='PetID')\n",
        "df_test_sent.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8779cd71f1d719c0767068cf6efb1c3842c908e2",
        "id": "0BiCKlnVP8pO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "alist = []\n",
        "for i in range(len(df_test_sent)):\n",
        "    alist.append(len(str(df_test_sent.iloc[i]['Description'])))\n",
        "# Create a column from the list\n",
        "df_test_sent['desc_len'] = alist\n",
        "df_test_sent.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "c5T6nUR7P8pQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_test_sent.head().transpose()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2dff818b29691e6da14c6bd05a26e75422f8909b",
        "id": "G1bhbSWCP8pT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get test image metadata\n",
        "path_test_img_meta = path/'test_metadata'\n",
        "\n",
        "tuplist = []\n",
        "for filename in os.listdir(path_test_img_meta):\n",
        "    if filename.endswith(\".json\"):\n",
        "      with open(path_test_img_meta/filename) as f:\n",
        "        data = json.load(f)\n",
        "        x = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['x']\n",
        "        y = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['y']        \n",
        "        tuplist.append( (filename[:filename.find('-')], x, y) )\n",
        "\n",
        "tuplist[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "24a8daa8bf3ae256d4daf77fc51e0ef710505491",
        "id": "1nxsbBkYP8pX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_test_meta = pd.DataFrame(tuplist, columns=['PetID', 'vert_x', 'vert_y'])\n",
        "df_test_meta.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5e4ceb6dcafae0181ec4c5df0a4f2d351016a69f",
        "id": "VTfEwvq4P8pZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# aggregate test image metadata\n",
        "grouped1 = df_test_meta.groupby('PetID',as_index=False)['vert_x', 'vert_y'].agg({'vert_min':'min', 'vert_max':'max', \n",
        "                                                                               'vert_mean':'mean'})\n",
        "grouped1.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "aa76870bd75ec99973191171f789a95ec5a92476",
        "id": "u7GhluSnP8pd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "grouped1.columns = ['PetID','PetID1','vert_x_min','vert_y_min','PetID2','vert_x_max','vert_y_max','PetID3','vert_x_mean','vert_y_mean']\n",
        "grouped1 = grouped1[['PetID','vert_x_min','vert_y_min','vert_x_max','vert_y_max','vert_x_mean','vert_y_mean']]\n",
        "grouped1.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2581b8f3a4e4a6cc463959c6b7d81dfde382054d",
        "id": "Rn8kiOf_P8pf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_test_meta = pd.merge(df_test_sent, grouped1, how='left', on='PetID')\n",
        "df_test_meta.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "baf0ea6d8342ff0e6427547a09389a256f497470",
        "id": "ZUs_fq6QP8ph",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# list(df_train_meta.columns.values)\n",
        "# for col in df_train_meta:\n",
        "#     print( col, \": \", len(df_train_meta[col].unique()) )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "75b3d1af25cb58adb59bfa722397c6e69129173a",
        "id": "L_FqdCDSP8pk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dep_var = 'AdoptionSpeed'\n",
        "cat_names = ['Type', 'Breed1', 'Breed2', 'Gender', 'Color1', 'Color2', 'Color3',\n",
        "             'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Quantity', \n",
        "             'State', 'VideoAmt', 'PhotoAmt'] \n",
        "cont_names = ['Age', 'Fee', 'magnitude', 'score', 'desc_len', 'vert_x_mean', 'vert_y_mean']\n",
        "procs = [FillMissing, Categorify, Normalize]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c23cbdceae37084ff0e13df5176b19f8e60783b2",
        "id": "l9FlsJNKP8pn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = (TabularList.from_df(df_train_meta, path='.', cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
        "                           .split_by_rand_pct(0.2, seed=420)\n",
        "                           .label_from_df(cols=dep_var)\n",
        "                          .add_test(TabularList.from_df(df_test_meta, path='.', cat_names=cat_names, cont_names=cont_names, procs=procs))\n",
        "                           .databunch())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "eb483496b1ff1e9e87590793aecd910be9813c11",
        "id": "W4cr2i8SP8pr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data.show_batch(rows=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "68522a38b49c9664174935483d22efb2b88a44b8",
        "id": "5izZ_uy1P8pu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "kappa = KappaScore()\n",
        "kappa.weights = \"quadratic\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c166b56fc911a956d80bb7e11c08a668886067de",
        "id": "EA0VW_knP8pw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = tabular_learner(data, layers=[1000, 500], ps=[0.001,0.01], emb_drop=0.1, metrics=[accuracy, kappa])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3920d38af0f204e1450fcbf6b9f8d69b2c1dddcf",
        "scrolled": true,
        "id": "FXBrQxzOP8pz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(5, 1e-2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2798a1a57dea47746040bae3ddb904e39c97d363",
        "id": "iHUvF0eIP8p2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot_losses()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f56bc66bc2d5880cb3983d294f4bde5fa21a8dd9",
        "id": "iWlGF1jDP8p4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b3c50ababe1af24da85d160402658747e01ca94c",
        "id": "WYB7IsFmP8p-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f9e09ace3de70b4c22ce186914fdf3ab84ad4a74",
        "id": "0Da-G5TFP8qA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(3, 1e-5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0644f02a7cf5c5ff78f174c3014db000592bd934",
        "id": "mv_TfJ1TP8qC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get predictions for test data\n",
        "test_preds=learn.get_preds(DatasetType.Test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4dd75556a95b3d6cbea8c891f1c4a92ad52b0cc3",
        "id": "Oms7hHoHP8qG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_test_meta[\"AdoptionSpeed\"] = test_preds[0].argmax(dim=1)\n",
        "result = df_test_meta[[\"PetID\",\"AdoptionSpeed\"]]\n",
        "result.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b0f9faaad0b7054d748cf514ef5d7ec2e0c436c7",
        "id": "DZFJRibbP8qI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result.to_csv(\"submission.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "96e1f800c3209ea96eb690a8f0e6d100e192fa52",
        "id": "AxaZIgSPP8qK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Nd4bmToKP8qO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(os.listdir(\"./\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "5y-1jS_5P8qQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}